Source code for statsmodels.tsa.statespace.dynamic_factor_mq
"""
Dynamic factor model.
Author: Chad Fulton
License: BSD-3
"""
from statsmodels.compat.pandas import MONTH_END, QUARTER_END
from collections import OrderedDict
from warnings import warn
import numpy as np
import pandas as pd
from scipy.linalg import cho_factor, cho_solve, LinAlgError
from statsmodels.tools.data import _is_using_pandas
from statsmodels.tools.validation import int_like
from statsmodels.tools.decorators import cache_readonly
from statsmodels.regression.linear_model import OLS
from statsmodels.genmod.generalized_linear_model import GLM
from statsmodels.multivariate.pca import PCA
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.statespace._quarterly_ar1 import QuarterlyAR1
from statsmodels.tsa.vector_ar.var_model import VAR
from statsmodels.tools.tools import Bunch
from statsmodels.tools.validation import string_like
from statsmodels.tsa.tsatools import lagmat
from statsmodels.tsa.statespace import mlemodel, initialization
from statsmodels.tsa.statespace.tools import (
companion_matrix, is_invertible, constrain_stationary_univariate,
constrain_stationary_multivariate, unconstrain_stationary_univariate,
unconstrain_stationary_multivariate)
from statsmodels.tsa.statespace.kalman_smoother import (
SMOOTHER_STATE, SMOOTHER_STATE_COV, SMOOTHER_STATE_AUTOCOV)
from statsmodels.base.data import PandasData
from statsmodels.iolib.table import SimpleTable
from statsmodels.iolib.summary import Summary
from statsmodels.iolib.tableformatting import fmt_params
class FactorBlock(dict):
"""
Helper class for describing and indexing a block of factors.
Parameters
----------
factor_names : tuple of str
Tuple of factor names in the block (in the order that they will appear
in the state vector).
factor_order : int
Order of the vector autoregression governing the factor block dynamics.
endog_factor_map : pd.DataFrame
Mapping from endog variable names to factor names.
state_offset : int
Offset of this factor block in the state vector.
has_endog_Q : bool
Flag if the model contains quarterly data.
Notes
-----
The goal of this class is, in particular, to make it easier to retrieve
indexes of subsets of the state vector that are associated with a
particular block of factors.
- `factors_ix` is a matrix of indices, with rows corresponding to factors
in the block and columns corresponding to lags
- `factors` is vec(factors_ix) (i.e. it stacks columns, so that it is
`factors_ix.ravel(order='F')`). Thinking about a VAR system, the first
k*p elements correspond to the equation for the first variable. The next
k*p elements correspond to the equation for the second variable, and so
on. It contains all of the lags in the state vector, which is max(5, p)
- `factors_ar` is the subset of `factors` that have nonzero coefficients,
so it contains lags up to p.
- `factors_L1` only contains the first lag of the factors
- `factors_L1_5` contains the first - fifth lags of the factors
"""
def __init__(self, factor_names, factor_order, endog_factor_map,
state_offset, k_endog_Q):
self.factor_names = factor_names
self.k_factors = len(self.factor_names)
self.factor_order = factor_order
self.endog_factor_map = endog_factor_map.loc[:, factor_names]
self.state_offset = state_offset
self.k_endog_Q = k_endog_Q
if self.k_endog_Q > 0:
self._factor_order = max(5, self.factor_order)
else:
self._factor_order = self.factor_order
self.k_states = self.k_factors * self._factor_order
# Save items
self['factors'] = self.factors
self['factors_ar'] = self.factors_ar
self['factors_ix'] = self.factors_ix
self['factors_L1'] = self.factors_L1
self['factors_L1_5'] = self.factors_L1_5
@property
def factors_ix(self):
"""Factor state index array, shaped (k_factors, lags)."""
# i.e. the position in the state vector of the second lag of the third
# factor is factors_ix[2, 1]
# ravel(order='F') gives e.g (f0.L1, f1.L1, f0.L2, f1.L2, f0.L3, ...)
# while
# ravel(order='C') gives e.g (f0.L1, f0.L2, f0.L3, f1.L1, f1.L2, ...)
o = self.state_offset
return np.reshape(o + np.arange(self.k_factors * self._factor_order),
(self._factor_order, self.k_factors)).T
@property
def factors(self):
"""Factors and all lags in the state vector (max(5, p))."""
# Note that this is equivalent to factors_ix with ravel(order='F')
o = self.state_offset
return np.s_[o:o + self.k_factors * self._factor_order]
@property
def factors_ar(self):
"""Factors and all lags used in the factor autoregression (p)."""
o = self.state_offset
return np.s_[o:o + self.k_factors * self.factor_order]
@property
def factors_L1(self):
"""Factors (first block / lag only)."""
o = self.state_offset
return np.s_[o:o + self.k_factors]
@property
def factors_L1_5(self):
"""Factors plus four lags."""
o = self.state_offset
return np.s_[o:o + self.k_factors * 5]
class DynamicFactorMQStates(dict):
"""
Helper class for describing and indexing the state vector.
Parameters
----------
k_endog_M : int
Number of monthly (or non-time-specific, if k_endog_Q=0) variables.
k_endog_Q : int
Number of quarterly variables.
endog_names : list
Names of the endogenous variables.
factors : int, list, or dict
Integer giving the number of (global) factors, a list with the names of
(global) factors, or a dictionary with:
- keys : names of endogenous variables
- values : lists of factor names.
If this is an integer, then the factor names will be 0, 1, ....
factor_orders : int or dict
Integer describing the order of the vector autoregression (VAR)
governing all factor block dynamics or dictionary with:
- keys : factor name or tuples of factor names in a block
- values : integer describing the VAR order for that factor block
If a dictionary, this defines the order of the factor blocks in the
state vector. Otherwise, factors are ordered so that factors that load
on more variables come first (and then alphabetically, to break ties).
factor_multiplicities : int or dict
This argument provides a convenient way to specify multiple factors
that load identically on variables. For example, one may want two
"global" factors (factors that load on all variables) that evolve
jointly according to a VAR. One could specify two global factors in the
`factors` argument and specify that they are in the same block in the
`factor_orders` argument, but it is easier to specify a single global
factor in the `factors` argument, and set the order in the
`factor_orders` argument, and then set the factor multiplicity to 2.
This argument must be an integer describing the factor multiplicity for
all factors or dictionary with:
- keys : factor name
- values : integer describing the factor multiplicity for the factors
in the given block
idiosyncratic_ar1 : bool
Whether or not to model the idiosyncratic component for each series as
an AR(1) process. If False, the idiosyncratic component is instead
modeled as white noise.
Attributes
----------
k_endog : int
Total number of endogenous variables.
k_states : int
Total number of state variables (those associated with the factors and
those associated with the idiosyncratic disturbances).
k_posdef : int
Total number of state disturbance terms (those associated with the
factors and those associated with the idiosyncratic disturbances).
k_endog_M : int
Number of monthly (or non-time-specific, if k_endog_Q=0) variables.
k_endog_Q : int
Number of quarterly variables.
k_factors : int
Total number of factors. Note that factor multiplicities will have
already been expanded.
k_states_factors : int
The number of state variables associated with factors (includes both
factors and lags of factors included in the state vector).
k_posdef_factors : int
The number of state disturbance terms associated with factors.
k_states_idio : int
Total number of state variables associated with idiosyncratic
disturbances.
k_posdef_idio : int
Total number of state disturbance terms associated with idiosyncratic
disturbances.
k_states_idio_M : int
The number of state variables associated with idiosyncratic
disturbances for monthly (or non-time-specific if there are no
quarterly variables) variables. If the disturbances are AR(1), then
this will be equal to `k_endog_M`, otherwise it will be equal to zero.
k_states_idio_Q : int
The number of state variables associated with idiosyncratic
disturbances for quarterly variables. This will always be equal to
`k_endog_Q * 5`, even if the disturbances are not AR(1).
k_posdef_idio_M : int
The number of state disturbance terms associated with idiosyncratic
disturbances for monthly (or non-time-specific if there are no
quarterly variables) variables. If the disturbances are AR(1), then
this will be equal to `k_endog_M`, otherwise it will be equal to zero.
k_posdef_idio_Q : int
The number of state disturbance terms associated with idiosyncratic
disturbances for quarterly variables. This will always be equal to
`k_endog_Q`, even if the disturbances are not AR(1).
idiosyncratic_ar1 : bool
Whether or not to model the idiosyncratic component for each series as
an AR(1) process.
factor_blocks : list of FactorBlock
List of `FactorBlock` helper instances for each factor block.
factor_names : list of str
List of factor names.
factors : dict
Dictionary with:
- keys : names of endogenous variables
- values : lists of factor names.
Note that factor multiplicities will have already been expanded.
factor_orders : dict
Dictionary with:
- keys : tuple of factor names
- values : integer describing autoregression order
Note that factor multiplicities will have already been expanded.
max_factor_order : int
Maximum autoregression order across all factor blocks.
factor_block_orders : pd.Series
Series containing lag orders, with the factor block (a tuple of factor
names) as the index.
factor_multiplicities : dict
Dictionary with:
- keys : factor name
- values : integer describing the factor multiplicity for the factors
in the given block
endog_factor_map : dict
Dictionary with:
- keys : endog name
- values : list of factor names
loading_counts : pd.Series
Series containing number of endogenous variables loading on each
factor, with the factor name as the index.
block_loading_counts : dict
Dictionary with:
- keys : tuple of factor names
- values : average number of endogenous variables loading on the block
(note that average is over the factors in the block)
Notes
-----
The goal of this class is, in particular, to make it easier to retrieve
indexes of subsets of the state vector.
Note that the ordering of the factor blocks in the state vector is
determined by the `factor_orders` argument if a dictionary. Otherwise,
factors are ordered so that factors that load on more variables come first
(and then alphabetically, to break ties).
- `factors_L1` is an array with the indexes of first lag of the factors
from each block. Ordered first by block, and then by lag.
- `factors_L1_5` is an array with the indexes contains the first - fifth
lags of the factors from each block. Ordered first by block, and then by
lag.
- `factors_L1_5_ix` is an array shaped (5, k_factors) with the indexes
of the first - fifth lags of the factors from each block.
- `idio_ar_L1` is an array with the indexes of the first lag of the
idiosyncratic AR states, both monthly (if appliable) and quarterly.
- `idio_ar_M` is a slice with the indexes of the idiosyncratic disturbance
states for the monthly (or non-time-specific if there are no quarterly
variables) variables. It is an empty slice if
`idiosyncratic_ar1 = False`.
- `idio_ar_Q` is a slice with the indexes of the idiosyncratic disturbance
states and all lags, for the quarterly variables. It is an empty slice if
there are no quarterly variable.
- `idio_ar_Q_ix` is an array shaped (k_endog_Q, 5) with the indexes of the
first - fifth lags of the idiosyncratic disturbance states for the
quarterly variables.
- `endog_factor_iloc` is a list of lists, with entries for each endogenous
variable. The entry for variable `i`, `endog_factor_iloc[i]` is a list of
indexes of the factors that variable `i` loads on. This does not include
any lags, but it can be used with e.g. `factors_L1_5_ix` to get lags.
"""
def __init__(self, k_endog_M, k_endog_Q, endog_names, factors,
factor_orders, factor_multiplicities, idiosyncratic_ar1):
# Save model parameterization
self.k_endog_M = k_endog_M
self.k_endog_Q = k_endog_Q
self.k_endog = self.k_endog_M + self.k_endog_Q
self.idiosyncratic_ar1 = idiosyncratic_ar1
# Validate factor-related inputs
factors_is_int = np.issubdtype(type(factors), np.integer)
factors_is_list = isinstance(factors, (list, tuple))
orders_is_int = np.issubdtype(type(factor_orders), np.integer)
if factor_multiplicities is None:
factor_multiplicities = 1
mult_is_int = np.issubdtype(type(factor_multiplicities), np.integer)
if not (factors_is_int or factors_is_list or
isinstance(factors, dict)):
raise ValueError('`factors` argument must an integer number of'
' factors, a list of global factor names, or a'
' dictionary, mapping observed variables to'
' factors.')
if not (orders_is_int or isinstance(factor_orders, dict)):
raise ValueError('`factor_orders` argument must either be an'
' integer or a dictionary.')
if not (mult_is_int or isinstance(factor_multiplicities, dict)):
raise ValueError('`factor_multiplicities` argument must either be'
' an integer or a dictionary.')
# Expand integers
# If `factors` is an integer, we assume that it denotes the number of
# global factors (factors that load on each variable)
if factors_is_int or factors_is_list:
# Validate this here for a more informative error message
if ((factors_is_int and factors == 0) or
(factors_is_list and len(factors) == 0)):
raise ValueError('The model must contain at least one factor.')
if factors_is_list:
factor_names = list(factors)
else:
factor_names = [f'{i}' for i in range(factors)]
factors = {name: factor_names[:] for name in endog_names}
_factor_names = []
for val in factors.values():
_factor_names.extend(val)
factor_names = set(_factor_names)
if orders_is_int:
factor_orders = {factor_name: factor_orders
for factor_name in factor_names}
if mult_is_int:
factor_multiplicities = {factor_name: factor_multiplicities
for factor_name in factor_names}
# Apply the factor multiplicities
factors, factor_orders = self._apply_factor_multiplicities(
factors, factor_orders, factor_multiplicities)
# Save the (potentially expanded) variables
self.factors = factors
self.factor_orders = factor_orders
self.factor_multiplicities = factor_multiplicities
# Get the mapping between endog and factors
self.endog_factor_map = self._construct_endog_factor_map(
factors, endog_names)
self.k_factors = self.endog_factor_map.shape[1]
# Validate number of factors
# TODO: could do more extensive validation here.
if self.k_factors > self.k_endog_M:
raise ValueError(f'Number of factors ({self.k_factors}) cannot be'
' greater than the number of monthly endogenous'
f' variables ({self.k_endog_M}).')
# Get `loading_counts`: factor -> # endog loading on the factor
self.loading_counts = (
self.endog_factor_map.sum(axis=0).rename('count')
.reset_index().sort_values(['count', 'factor'],
ascending=[False, True])
.set_index('factor'))
# `block_loading_counts`: block -> average of (# loading on factor)
# across each factor in the block
block_loading_counts = {
block: np.atleast_1d(
self.loading_counts.loc[list(block), 'count']).mean(axis=0)
for block in factor_orders.keys()}
ix = pd.Index(block_loading_counts.keys(), tupleize_cols=False,
name='block')
self.block_loading_counts = pd.Series(
list(block_loading_counts.values()),
index=ix, name='count').to_frame().sort_values(
['count', 'block'], ascending=[False, True])['count']
# Get the mapping between factor blocks and VAR order
# `factor_block_orders`: pd.Series of factor block -> lag order
ix = pd.Index(factor_orders.keys(), tupleize_cols=False, name='block')
self.factor_block_orders = pd.Series(
list(factor_orders.values()), index=ix, name='order')
# If the `factor_orders` variable was an integer, then it did not
# define an ordering for the factor blocks. In this case, we use the
# loading counts to do so. This ensures that e.g. global factors are
# listed first.
if orders_is_int:
keys = self.block_loading_counts.keys()
self.factor_block_orders = self.factor_block_orders.loc[keys]
self.factor_block_orders.index.name = 'block'
# Define factor_names based on factor_block_orders (instead of on those
# from `endog_factor_map`) to (a) make sure that factors are allocated
# to only one block, and (b) order the factor names to be consistent
# with the block definitions.
factor_names = pd.Series(
np.concatenate(list(self.factor_block_orders.index)))
missing = [name for name in self.endog_factor_map.columns
if name not in factor_names.tolist()]
if len(missing):
ix = pd.Index([(factor_name,) for factor_name in missing],
tupleize_cols=False, name='block')
default_block_orders = pd.Series(np.ones(len(ix), dtype=int),
index=ix, name='order')
self.factor_block_orders = pd.concat(
[self.factor_block_orders, default_block_orders])
factor_names = pd.Series(
np.concatenate(list(self.factor_block_orders.index)))
duplicates = factor_names.duplicated()
if duplicates.any():
duplicate_names = set(factor_names[duplicates])
raise ValueError('Each factor can be assigned to at most one'
' block of factors in `factor_orders`.'
f' Duplicate entries for {duplicate_names}')
self.factor_names = factor_names.tolist()
self.max_factor_order = np.max(self.factor_block_orders)
# Re-order the columns of the endog factor mapping to reflect the
# orderings of endog_names and factor_names
self.endog_factor_map = (
self.endog_factor_map.loc[endog_names, factor_names])
# Create factor block helpers, and get factor-related state and posdef
# dimensions
self.k_states_factors = 0
self.k_posdef_factors = 0
state_offset = 0
self.factor_blocks = []
for factor_names, factor_order in self.factor_block_orders.items():
block = FactorBlock(factor_names, factor_order,
self.endog_factor_map, state_offset,
self.k_endog_Q)
self.k_states_factors += block.k_states
self.k_posdef_factors += block.k_factors
state_offset += block.k_states
self.factor_blocks.append(block)
# Idiosyncratic state dimensions
self.k_states_idio_M = self.k_endog_M if idiosyncratic_ar1 else 0
self.k_states_idio_Q = self.k_endog_Q * 5
self.k_states_idio = self.k_states_idio_M + self.k_states_idio_Q
# Idiosyncratic posdef dimensions
self.k_posdef_idio_M = self.k_endog_M if self.idiosyncratic_ar1 else 0
self.k_posdef_idio_Q = self.k_endog_Q
self.k_posdef_idio = self.k_posdef_idio_M + self.k_posdef_idio_Q
# Total states, posdef
self.k_states = self.k_states_factors + self.k_states_idio
self.k_posdef = self.k_posdef_factors + self.k_posdef_idio
# Cache
self._endog_factor_iloc = None
def _apply_factor_multiplicities(self, factors, factor_orders,
factor_multiplicities):
"""
Expand `factors` and `factor_orders` to account for factor multiplity.
For example, if there is a `global` factor with multiplicity 2, then
this method expands that into `global.1` and `global.2` in both the
`factors` and `factor_orders` dictionaries.
Parameters
----------
factors : dict
Dictionary of {endog_name: list of factor names}
factor_orders : dict
Dictionary of {tuple of factor names: factor order}
factor_multiplicities : dict
Dictionary of {factor name: factor multiplicity}
Returns
-------
new_factors : dict
Dictionary of {endog_name: list of factor names}, with factor names
expanded to incorporate multiplicities.
new_factors : dict
Dictionary of {tuple of factor names: factor order}, with factor
names in each tuple expanded to incorporate multiplicities.
"""
# Expand the factors to account for the multiplicities
new_factors = {}
for endog_name, factors_list in factors.items():
new_factor_list = []
for factor_name in factors_list:
n = factor_multiplicities.get(factor_name, 1)
if n > 1:
new_factor_list += [f'{factor_name}.{i + 1}'
for i in range(n)]
else:
new_factor_list.append(factor_name)
new_factors[endog_name] = new_factor_list
# Expand the factor orders to account for the multiplicities
new_factor_orders = {}
for block, factor_order in factor_orders.items():
if not isinstance(block, tuple):
block = (block,)
new_block = []
for factor_name in block:
n = factor_multiplicities.get(factor_name, 1)
if n > 1:
new_block += [f'{factor_name}.{i + 1}'
for i in range(n)]
else:
new_block += [factor_name]
new_factor_orders[tuple(new_block)] = factor_order
return new_factors, new_factor_orders
def _construct_endog_factor_map(self, factors, endog_names):
"""
Construct mapping of observed variables to factors.
Parameters
----------
factors : dict
Dictionary of {endog_name: list of factor names}
endog_names : list of str
List of the names of the observed variables.
Returns
-------
endog_factor_map : pd.DataFrame
Boolean dataframe with `endog_names` as the index and the factor
names (computed from the `factors` input) as the columns. Each cell
is True if the associated factor is allowed to load on the
associated observed variable.
"""
# Validate that all entries in the factors dictionary have associated
# factors
missing = []
for key, value in factors.items():
if not isinstance(value, (list, tuple)) or len(value) == 0:
missing.append(key)
if len(missing):
raise ValueError('Each observed variable must be mapped to at'
' least one factor in the `factors` dictionary.'
f' Variables missing factors are: {missing}.')
# Validate that we have been told about the factors for each endog
# variable. This is because it doesn't make sense to include an
# observed variable that doesn't load on any factor
missing = set(endog_names).difference(set(factors.keys()))
if len(missing):
raise ValueError('If a `factors` dictionary is provided, then'
' it must include entries for each observed'
f' variable. Missing variables are: {missing}.')
# Figure out the set of factor names
# (0 is just a dummy value for the dict - we just do it this way to
# collect the keys, in order, without duplicates.)
factor_names = {}
for key, value in factors.items():
if isinstance(value, str):
factor_names[value] = 0
else:
factor_names.update({v: 0 for v in value})
factor_names = list(factor_names.keys())
k_factors = len(factor_names)
endog_factor_map = pd.DataFrame(
np.zeros((self.k_endog, k_factors), dtype=bool),
index=pd.Index(endog_names, name='endog'),
columns=pd.Index(factor_names, name='factor'))
for key, value in factors.items():
endog_factor_map.loc[key, value] = True
return endog_factor_map
@property
def factors_L1(self):
"""Factors."""
ix = np.arange(self.k_states_factors)
iloc = tuple(ix[block.factors_L1] for block in self.factor_blocks)
return np.concatenate(iloc)
@property
def factors_L1_5_ix(self):
"""Factors plus any lags, index shaped (5, k_factors)."""
ix = np.arange(self.k_states_factors)
iloc = []
for block in self.factor_blocks:
iloc.append(ix[block.factors_L1_5].reshape(5, block.k_factors))
return np.concatenate(iloc, axis=1)
@property
def idio_ar_L1(self):
"""Idiosyncratic AR states, (first block / lag only)."""
ix1 = self.k_states_factors
if self.idiosyncratic_ar1:
ix2 = ix1 + self.k_endog
else:
ix2 = ix1 + self.k_endog_Q
return np.s_[ix1:ix2]
@property
def idio_ar_M(self):
"""Idiosyncratic AR states for monthly variables."""
ix1 = self.k_states_factors
ix2 = ix1
if self.idiosyncratic_ar1:
ix2 += self.k_endog_M
return np.s_[ix1:ix2]
@property
def idio_ar_Q(self):
"""Idiosyncratic AR states and all lags for quarterly variables."""
# Note that this is equivalent to idio_ar_Q_ix with ravel(order='F')
ix1 = self.k_states_factors
if self.idiosyncratic_ar1:
ix1 += self.k_endog_M
ix2 = ix1 + self.k_endog_Q * 5
return np.s_[ix1:ix2]
@property
def idio_ar_Q_ix(self):
"""Idiosyncratic AR (quarterly) state index, (k_endog_Q, lags)."""
# i.e. the position in the state vector of the second lag of the third
# quarterly variable is idio_ar_Q_ix[2, 1]
# ravel(order='F') gives e.g (y1.L1, y2.L1, y1.L2, y2.L3, y1.L3, ...)
# while
# ravel(order='C') gives e.g (y1.L1, y1.L2, y1.L3, y2.L1, y2.L2, ...)
start = self.k_states_factors
if self.idiosyncratic_ar1:
start += self.k_endog_M
return (start + np.reshape(
np.arange(5 * self.k_endog_Q), (5, self.k_endog_Q)).T)
@property
def endog_factor_iloc(self):
"""List of list of int, factor indexes for each observed variable."""
# i.e. endog_factor_iloc[i] is a list of integer locations of the
# factors that load on the ith observed variable
if self._endog_factor_iloc is None:
ilocs = []
for i in range(self.k_endog):
ilocs.append(np.where(self.endog_factor_map.iloc[i])[0])
self._endog_factor_iloc = ilocs
return self._endog_factor_iloc
def __getitem__(self, key):
"""
Use square brackets to access index / slice elements.
This is convenient in highlighting the indexing / slice quality of
these attributes in the code below.
"""
if key in ['factors_L1', 'factors_L1_5_ix', 'idio_ar_L1', 'idio_ar_M',
'idio_ar_Q', 'idio_ar_Q_ix']:
return getattr(self, key)
else:
raise KeyError(key)
[docs]
class DynamicFactorMQ(mlemodel.MLEModel):
r"""
Dynamic factor model with EM algorithm; option for monthly/quarterly data.
Implementation of the dynamic factor model of Bańbura and Modugno (2014)
([1]_) and Bańbura, Giannone, and Reichlin (2011) ([2]_). Uses the EM
algorithm for parameter fitting, and so can accommodate a large number of
left-hand-side variables. Specifications can include any collection of
blocks of factors, including different factor autoregression orders, and
can include AR(1) processes for idiosyncratic disturbances. Can
incorporate monthly/quarterly mixed frequency data along the lines of
Mariano and Murasawa (2011) ([4]_). A special case of this model is the
Nowcasting model of Bok et al. (2017) ([3]_). Moreover, this model can be
used to compute the news associated with updated data releases.
Parameters
----------
endog : array_like
Observed time-series process :math:`y`. See the "Notes" section for
details on how to set up a model with monthly/quarterly mixed frequency
data.
k_endog_monthly : int, optional
If specifying a monthly/quarterly mixed frequency model in which the
provided `endog` dataset contains both the monthly and quarterly data,
this variable should be used to indicate how many of the variables
are monthly. Note that when using the `k_endog_monthly` argument, the
columns with monthly variables in `endog` should be ordered first, and
the columns with quarterly variables should come afterwards. See the
"Notes" section for details on how to set up a model with
monthly/quarterly mixed frequency data.
factors : int, list, or dict, optional
Integer giving the number of (global) factors, a list with the names of
(global) factors, or a dictionary with:
- keys : names of endogenous variables
- values : lists of factor names.
If this is an integer, then the factor names will be 0, 1, .... The
default is a single factor that loads on all variables. Note that there
cannot be more factors specified than there are monthly variables.
factor_orders : int or dict, optional
Integer describing the order of the vector autoregression (VAR)
governing all factor block dynamics or dictionary with:
- keys : factor name or tuples of factor names in a block
- values : integer describing the VAR order for that factor block
If a dictionary, this defines the order of the factor blocks in the
state vector. Otherwise, factors are ordered so that factors that load
on more variables come first (and then alphabetically, to break ties).
factor_multiplicities : int or dict, optional
This argument provides a convenient way to specify multiple factors
that load identically on variables. For example, one may want two
"global" factors (factors that load on all variables) that evolve
jointly according to a VAR. One could specify two global factors in the
`factors` argument and specify that they are in the same block in the
`factor_orders` argument, but it is easier to specify a single global
factor in the `factors` argument, and set the order in the
`factor_orders` argument, and then set the factor multiplicity to 2.
This argument must be an integer describing the factor multiplicity for
all factors or dictionary with:
- keys : factor name
- values : integer describing the factor multiplicity for the factors
in the given block
idiosyncratic_ar1 : bool
Whether or not to model the idiosyncratic component for each series as
an AR(1) process. If False, the idiosyncratic component is instead
modeled as white noise.
standardize : bool or tuple, optional
If a boolean, whether or not to standardize each endogenous variable to
have mean zero and standard deviation 1 before fitting the model. See
"Notes" for details about how this option works with postestimation
output. If a tuple (usually only used internally), then the tuple must
have length 2, with each element containing a Pandas series with index
equal to the names of the endogenous variables. The first element
should contain the mean values and the second element should contain
the standard deviations. Default is True.
endog_quarterly : pandas.Series or pandas.DataFrame
Observed quarterly variables. If provided, must be a Pandas Series or
DataFrame with a DatetimeIndex or PeriodIndex at the quarterly
frequency. See the "Notes" section for details on how to set up a model
with monthly/quarterly mixed frequency data.
init_t0 : bool, optional
If True, this option initializes the Kalman filter with the
distribution for :math:`\alpha_0` rather than :math:`\alpha_1`. See
the "Notes" section for more details. This option is rarely used except
for testing. Default is False.
obs_cov_diag : bool, optional
If True and if `idiosyncratic_ar1 is True`, then this option puts small
positive values in the observation disturbance covariance matrix. This
is not required for estimation and is rarely used except for testing.
(It is sometimes used to prevent numerical errors, for example those
associated with a positive semi-definite forecast error covariance
matrix at the first time step when using EM initialization, but state
space models in Statsmodels switch to the univariate approach in those
cases, and so do not need to use this trick). Default is False.
Notes
-----
The basic model is:
.. math::
y_t & = \Lambda f_t + \epsilon_t \\
f_t & = A_1 f_{t-1} + \dots + A_p f_{t-p} + u_t
where:
- :math:`y_t` is observed data at time t
- :math:`\epsilon_t` is idiosyncratic disturbance at time t (see below for
details, including modeling serial correlation in this term)
- :math:`f_t` is the unobserved factor at time t
- :math:`u_t \sim N(0, Q)` is the factor disturbance at time t
and:
- :math:`\Lambda` is referred to as the matrix of factor loadings
- :math:`A_i` are matrices of autoregression coefficients
Furthermore, we allow the idiosyncratic disturbances to be serially
correlated, so that, if `idiosyncratic_ar1=True`,
:math:`\epsilon_{i,t} = \rho_i \epsilon_{i,t-1} + e_{i,t}`, where
:math:`e_{i,t} \sim N(0, \sigma_i^2)`. If `idiosyncratic_ar1=False`,
then we instead have :math:`\epsilon_{i,t} = e_{i,t}`.
This basic setup can be found in [1]_, [2]_, [3]_, and [4]_.
We allow for two generalizations of this model:
1. Following [2]_, we allow multiple "blocks" of factors, which are
independent from the other blocks of factors. Different blocks can be
set to load on different subsets of the observed variables, and can be
specified with different lag orders.
2. Following [4]_ and [2]_, we allow mixed frequency models in which both
monthly and quarterly data are used. See the section on "Mixed frequency
models", below, for more details.
Additional notes:
- The observed data may contain arbitrary patterns of missing entries.
**EM algorithm**
This model contains a potentially very large number of parameters, and it
can be difficult and take a prohibitively long time to numerically optimize
the likelihood function using quasi-Newton methods. Instead, the default
fitting method in this model uses the EM algorithm, as detailed in [1]_.
As a result, the model can accommodate datasets with hundreds of
observed variables.
**Mixed frequency data**
This model can handle mixed frequency data in two ways. In this section,
we only briefly describe this, and refer readers to [2]_ and [4]_ for all
details.
First, because there can be arbitrary patterns of missing data in the
observed vector, one can simply include lower frequency variables as
observed in a particular higher frequency period, and missing otherwise.
For example, in a monthly model, one could include quarterly data as
occurring on the third month of each quarter. To use this method, one
simply needs to combine the data into a single dataset at the higher
frequency that can be passed to this model as the `endog` argument.
However, depending on the type of variables used in the analysis and the
assumptions about the data generating process, this approach may not be
valid.
For example, suppose that we are interested in the growth rate of real GDP,
which is measured at a quarterly frequency. If the basic factor model is
specified at a monthly frequency, then the quarterly growth rate in the
third month of each quarter -- which is what we actually observe -- is
approximated by a particular weighted average of unobserved monthly growth
rates. We need to take this particular weight moving average into account
in constructing our model, and this is what the second approach does.
The second approach follows [2]_ and [4]_ in constructing a state space
form to explicitly model the quarterly growth rates in terms of the
unobserved monthly growth rates. To use this approach, there are two
methods:
1. Combine the monthly and quarterly data into a single dataset at the
monthly frequency, with the monthly data in the first columns and the
quarterly data in the last columns. Pass this dataset to the model as
the `endog` argument and give the number of the variables that are
monthly as the `k_endog_monthly` argument.
2. Construct a monthly dataset as a Pandas DataFrame with a DatetimeIndex
or PeriodIndex at the monthly frequency and separately construct a
quarterly dataset as a Pandas DataFrame with a DatetimeIndex or
PeriodIndex at the quarterly frequency. Pass the monthly DataFrame to
the model as the `endog` argument and pass the quarterly DataFrame to
the model as the `endog_quarterly` argument.
Note that this only incorporates one particular type of mixed frequency
data. See also Banbura et al. (2013). "Now-Casting and the Real-Time Data
Flow." for discussion about other types of mixed frequency data that are
not supported by this framework.
**Nowcasting and the news**
Through its support for monthly/quarterly mixed frequency data, this model
can allow for the nowcasting of quarterly variables based on monthly
observations. In particular, [2]_ and [3]_ use this model to construct
nowcasts of real GDP and analyze the impacts of "the news", derived from
incoming data on a real-time basis. This latter functionality can be
accessed through the `news` method of the results object.
**Standardizing data**
As is often the case in formulating a dynamic factor model, we do not
explicitly account for the mean of each observed variable. Instead, the
default behavior is to standardize each variable prior to estimation. Thus
if :math:`y_t` are the given observed data, the dynamic factor model is
actually estimated on the standardized data defined by:
.. math::
x_{i, t} = (y_{i, t} - \bar y_i) / s_i
where :math:`\bar y_i` is the sample mean and :math:`s_i` is the sample
standard deviation.
By default, if standardization is applied prior to estimation, results such
as in-sample predictions, out-of-sample forecasts, and the computation of
the "news" are reported in the scale of the original data (i.e. the model
output has the reverse transformation applied before it is returned to the
user).
Standardization can be disabled by passing `standardization=False` to the
model constructor.
**Identification of factors and loadings**
The estimated factors and the factor loadings in this model are only
identified up to an invertible transformation. As described in (the working
paper version of) [2]_, while it is possible to impose normalizations to
achieve identification, the EM algorithm does will converge regardless.
Moreover, for nowcasting and forecasting purposes, identification is not
required. This model does not impose any normalization to identify the
factors and the factor loadings.
**Miscellaneous**
There are two arguments available in the model constructor that are rarely
used but which deserve a brief mention: `init_t0` and `obs_cov_diag`. These
arguments are provided to allow exactly matching the output of other
packages that have slight differences in how the underlying state space
model is set up / applied.
- `init_t0`: state space models in Statsmodels follow Durbin and Koopman in
initializing the model with :math:`\alpha_1 \sim N(a_1, P_1)`. Other
implementations sometimes initialize instead with
:math:`\alpha_0 \sim N(a_0, P_0)`. We can accommodate this by prepending
a row of NaNs to the observed dataset.
- `obs_cov_diag`: the state space form in [1]_ incorporates non-zero (but
very small) diagonal elements for the observation disturbance covariance
matrix.
Examples
--------
Constructing and fitting a `DynamicFactorMQ` model.
>>> data = sm.datasets.macrodata.load_pandas().data.iloc[-100:]
>>> data.index = pd.period_range(start='1984Q4', end='2009Q3', freq='Q')
>>> endog = data[['infl', 'tbilrate']].resample('M').last()
>>> endog_Q = np.log(data[['realgdp', 'realcons']]).diff().iloc[1:] * 400
**Basic usage**
In the simplest case, passing only the `endog` argument results in a model
with a single factor that follows an AR(1) process. Note that because we
are not also providing an `endog_quarterly` dataset, `endog` can be a numpy
array or Pandas DataFrame with any index (it does not have to be monthly).
The `summary` method can be useful in checking the model specification.
>>> mod = sm.tsa.DynamicFactorMQ(endog)
>>> print(mod.summary())
Model Specification: Dynamic Factor Model
==========================================================================
Model: Dynamic Factor Model # of monthly variables: 2
+ 1 factors in 1 blocks # of factors: 1
+ AR(1) idiosyncratic Idiosyncratic disturbances: AR(1)
Sample: 1984-10 Standardize variables: True
- 2009-09
Observed variables / factor loadings
========================
Dep. variable 0
------------------------
infl X
tbilrate X
Factor blocks:
=====================
block order
---------------------
0 1
=====================
**Factors**
With `factors=2`, there will be two independent factors that will each
evolve according to separate AR(1) processes.
>>> mod = sm.tsa.DynamicFactorMQ(endog, factors=2)
>>> print(mod.summary())
Model Specification: Dynamic Factor Model
==========================================================================
Model: Dynamic Factor Model # of monthly variables: 2
+ 2 factors in 2 blocks # of factors: 2
+ AR(1) idiosyncratic Idiosyncratic disturbances: AR(1)
Sample: 1984-10 Standardize variables: True
- 2009-09
Observed variables / factor loadings
===================================
Dep. variable 0 1
-----------------------------------
infl X X
tbilrate X X
Factor blocks:
=====================
block order
---------------------
0 1
1 1
=====================
**Factor multiplicities**
By instead specifying `factor_multiplicities=2`, we would still have two
factors, but they would be dependent and would evolve jointly according
to a VAR(1) process.
>>> mod = sm.tsa.DynamicFactorMQ(endog, factor_multiplicities=2)
>>> print(mod.summary())
Model Specification: Dynamic Factor Model
==========================================================================
Model: Dynamic Factor Model # of monthly variables: 2
+ 2 factors in 1 blocks # of factors: 2
+ AR(1) idiosyncratic Idiosyncratic disturbances: AR(1)
Sample: 1984-10 Standardize variables: True
- 2009-09
Observed variables / factor loadings
===================================
Dep. variable 0.1 0.2
-----------------------------------
infl X X
tbilrate X X
Factor blocks:
=====================
block order
---------------------
0.1, 0.2 1
=====================
**Factor orders**
In either of the above cases, we could extend the order of the (vector)
autoregressions by using the `factor_orders` argument. For example, the
below model would contain two independent factors that each evolve
according to a separate AR(2) process:
>>> mod = sm.tsa.DynamicFactorMQ(endog, factors=2, factor_orders=2)
>>> print(mod.summary())
Model Specification: Dynamic Factor Model
==========================================================================
Model: Dynamic Factor Model # of monthly variables: 2
+ 2 factors in 2 blocks # of factors: 2
+ AR(1) idiosyncratic Idiosyncratic disturbances: AR(1)
Sample: 1984-10 Standardize variables: True
- 2009-09
Observed variables / factor loadings
===================================
Dep. variable 0 1
-----------------------------------
infl X X
tbilrate X X
Factor blocks:
=====================
block order
---------------------
0 2
1 2
=====================
**Serial correlation in the idiosyncratic disturbances**
By default, the model allows each idiosyncratic disturbance terms to evolve
according to an AR(1) process. If preferred, they can instead be specified
to be serially independent by passing `ididosyncratic_ar1=False`.
>>> mod = sm.tsa.DynamicFactorMQ(endog, idiosyncratic_ar1=False)
>>> print(mod.summary())
Model Specification: Dynamic Factor Model
==========================================================================
Model: Dynamic Factor Model # of monthly variables: 2
+ 1 factors in 1 blocks # of factors: 1
+ iid idiosyncratic Idiosyncratic disturbances: iid
Sample: 1984-10 Standardize variables: True
- 2009-09
Observed variables / factor loadings
========================
Dep. variable 0
------------------------
infl X
tbilrate X
Factor blocks:
=====================
block order
---------------------
0 1
=====================
*Monthly / Quarterly mixed frequency*
To specify a monthly / quarterly mixed frequency model see the (Notes
section for more details about these models):
>>> mod = sm.tsa.DynamicFactorMQ(endog, endog_quarterly=endog_Q)
>>> print(mod.summary())
Model Specification: Dynamic Factor Model
==========================================================================
Model: Dynamic Factor Model # of monthly variables: 2
+ 1 factors in 1 blocks # of quarterly variables: 2
+ Mixed frequency (M/Q) # of factors: 1
+ AR(1) idiosyncratic Idiosyncratic disturbances: AR(1)
Sample: 1984-10 Standardize variables: True
- 2009-09
Observed variables / factor loadings
========================
Dep. variable 0
------------------------
infl X
tbilrate X
realgdp X
realcons X
Factor blocks:
=====================
block order
---------------------
0 1
=====================
*Customize observed variable / factor loadings*
To specify that certain that certain observed variables only load on
certain factors, it is possible to pass a dictionary to the `factors`
argument.
>>> factors = {'infl': ['global']
... 'tbilrate': ['global']
... 'realgdp': ['global', 'real']
... 'realcons': ['global', 'real']}
>>> mod = sm.tsa.DynamicFactorMQ(endog, endog_quarterly=endog_Q)
>>> print(mod.summary())
Model Specification: Dynamic Factor Model
==========================================================================
Model: Dynamic Factor Model # of monthly variables: 2
+ 2 factors in 2 blocks # of quarterly variables: 2
+ Mixed frequency (M/Q) # of factor blocks: 2
+ AR(1) idiosyncratic Idiosyncratic disturbances: AR(1)
Sample: 1984-10 Standardize variables: True
- 2009-09
Observed variables / factor loadings
===================================
Dep. variable global real
-----------------------------------
infl X
tbilrate X
realgdp X X
realcons X X
Factor blocks:
=====================
block order
---------------------
global 1
real 1
=====================
**Fitting parameters**
To fit the model, use the `fit` method. This method uses the EM algorithm
by default.
>>> mod = sm.tsa.DynamicFactorMQ(endog)
>>> res = mod.fit()
>>> print(res.summary())
Dynamic Factor Results
==========================================================================
Dep. Variable: ['infl', 'tbilrate'] No. Observations: 300
Model: Dynamic Factor Model Log Likelihood -127.909
+ 1 factors in 1 blocks AIC 271.817
+ AR(1) idiosyncratic BIC 301.447
Date: Tue, 04 Aug 2020 HQIC 283.675
Time: 15:59:11 EM Iterations 83
Sample: 10-31-1984
- 09-30-2009
Covariance Type: Not computed
Observation equation:
==============================================================
Factor loadings: 0 idiosyncratic: AR(1) var.
--------------------------------------------------------------
infl -0.67 0.39 0.73
tbilrate -0.63 0.99 0.01
Transition: Factor block 0
=======================================
L1.0 error variance
---------------------------------------
0 0.98 0.01
=======================================
Warnings:
[1] Covariance matrix not calculated.
*Displaying iteration progress*
To display information about the EM iterations, use the `disp` argument.
>>> mod = sm.tsa.DynamicFactorMQ(endog)
>>> res = mod.fit(disp=10)
EM start iterations, llf=-291.21
EM iteration 10, llf=-157.17, convergence criterion=0.053801
EM iteration 20, llf=-128.99, convergence criterion=0.0035545
EM iteration 30, llf=-127.97, convergence criterion=0.00010224
EM iteration 40, llf=-127.93, convergence criterion=1.3281e-05
EM iteration 50, llf=-127.92, convergence criterion=5.4725e-06
EM iteration 60, llf=-127.91, convergence criterion=2.8665e-06
EM iteration 70, llf=-127.91, convergence criterion=1.6999e-06
EM iteration 80, llf=-127.91, convergence criterion=1.1085e-06
EM converged at iteration 83, llf=-127.91,
convergence criterion=9.9004e-07 < tolerance=1e-06
**Results: forecasting, impulse responses, and more**
One the model is fitted, there are a number of methods available from the
results object. Some examples include:
*Forecasting*
>>> mod = sm.tsa.DynamicFactorMQ(endog)
>>> res = mod.fit()
>>> print(res.forecast(steps=5))
infl tbilrate
2009-10 1.784169 0.260401
2009-11 1.735848 0.305981
2009-12 1.730674 0.350968
2010-01 1.742110 0.395369
2010-02 1.759786 0.439194
*Impulse responses*
>>> mod = sm.tsa.DynamicFactorMQ(endog)
>>> res = mod.fit()
>>> print(res.impulse_responses(steps=5))
infl tbilrate
0 -1.511956 -1.341498
1 -1.483172 -1.315960
2 -1.454937 -1.290908
3 -1.427240 -1.266333
4 -1.400069 -1.242226
5 -1.373416 -1.218578
For other available methods (including in-sample prediction, simulation of
time series, extending the results to incorporate new data, and the news),
see the documentation for state space models.
References
----------
.. [1] Bańbura, Marta, and Michele Modugno.
"Maximum likelihood estimation of factor models on datasets with
arbitrary pattern of missing data."
Journal of Applied Econometrics 29, no. 1 (2014): 133-160.
.. [2] Bańbura, Marta, Domenico Giannone, and Lucrezia Reichlin.
"Nowcasting."
The Oxford Handbook of Economic Forecasting. July 8, 2011.
.. [3] Bok, Brandyn, Daniele Caratelli, Domenico Giannone,
Argia M. Sbordone, and Andrea Tambalotti. 2018.
"Macroeconomic Nowcasting and Forecasting with Big Data."
Annual Review of Economics 10 (1): 615-43.
https://doi.org/10.1146/annurev-economics-080217-053214.
.. [4] Mariano, Roberto S., and Yasutomo Murasawa.
"A coincident index, common factors, and monthly real GDP."
Oxford Bulletin of Economics and Statistics 72, no. 1 (2010): 27-46.
"""
def __init__(self, endog, k_endog_monthly=None, factors=1, factor_orders=1,
factor_multiplicities=None, idiosyncratic_ar1=True,
standardize=True, endog_quarterly=None, init_t0=False,
obs_cov_diag=False, **kwargs):
# Handle endog variables
if endog_quarterly is not None:
if k_endog_monthly is not None:
raise ValueError('If `endog_quarterly` is specified, then'
' `endog` must contain only monthly'
' variables, and so `k_endog_monthly` cannot'
' be specified since it will be inferred from'
' the shape of `endog`.')
endog, k_endog_monthly = self.construct_endog(
endog, endog_quarterly)
endog_is_pandas = _is_using_pandas(endog, None)
if endog_is_pandas:
if isinstance(endog, pd.Series):
endog = endog.to_frame()
else:
if np.ndim(endog) < 2:
endog = np.atleast_2d(endog).T
if k_endog_monthly is None:
k_endog_monthly = endog.shape[1]
if endog_is_pandas:
endog_names = endog.columns.tolist()
else:
if endog.shape[1] == 1:
endog_names = ['y']
else:
endog_names = [f'y{i + 1}' for i in range(endog.shape[1])]
self.k_endog_M = int_like(k_endog_monthly, 'k_endog_monthly')
self.k_endog_Q = endog.shape[1] - self.k_endog_M
# Compute helper for handling factors / state indexing
s = self._s = DynamicFactorMQStates(
self.k_endog_M, self.k_endog_Q, endog_names, factors,
factor_orders, factor_multiplicities, idiosyncratic_ar1)
# Save parameterization
self.factors = factors
self.factor_orders = factor_orders
self.factor_multiplicities = factor_multiplicities
self.endog_factor_map = self._s.endog_factor_map
self.factor_block_orders = self._s.factor_block_orders
self.factor_names = self._s.factor_names
self.k_factors = self._s.k_factors
self.k_factor_blocks = len(self.factor_block_orders)
self.max_factor_order = self._s.max_factor_order
self.idiosyncratic_ar1 = idiosyncratic_ar1
self.init_t0 = init_t0
self.obs_cov_diag = obs_cov_diag
if self.init_t0:
# TODO: test each of these options
if endog_is_pandas:
ix = pd.period_range(endog.index[0] - 1, endog.index[-1],
freq=endog.index.freq)
endog = endog.reindex(ix)
else:
endog = np.c_[[np.nan] * endog.shape[1], endog.T].T
# Standardize endog, if requested
# Note: endog_mean and endog_std will always each be 1-dimensional with
# length equal to the number of endog variables
if isinstance(standardize, tuple) and len(standardize) == 2:
endog_mean, endog_std = standardize
# Validate the input
n = endog.shape[1]
if (isinstance(endog_mean, pd.Series) and not
endog_mean.index.equals(pd.Index(endog_names))):
raise ValueError('Invalid value passed for `standardize`:'
' if a Pandas Series, must have index'
f' {endog_names}. Got {endog_mean.index}.')
else:
endog_mean = np.atleast_1d(endog_mean)
if (isinstance(endog_std, pd.Series) and not
endog_std.index.equals(pd.Index(endog_names))):
raise ValueError('Invalid value passed for `standardize`:'
' if a Pandas Series, must have index'
f' {endog_names}. Got {endog_std.index}.')
else:
endog_std = np.atleast_1d(endog_std)
if (np.shape(endog_mean) != (n,) or np.shape(endog_std) != (n,)):
raise ValueError('Invalid value passed for `standardize`: each'
f' element must be shaped ({n},).')
standardize = True
# Make sure we have Pandas if endog is Pandas
if endog_is_pandas:
endog_mean = pd.Series(endog_mean, index=endog_names)
endog_std = pd.Series(endog_std, index=endog_names)
elif standardize in [1, True]:
endog_mean = endog.mean(axis=0)
endog_std = endog.std(axis=0)
elif standardize in [0, False]:
endog_mean = np.zeros(endog.shape[1])
endog_std = np.ones(endog.shape[1])
else:
raise ValueError('Invalid value passed for `standardize`.')
self._endog_mean = endog_mean
self._endog_std = endog_std
self.standardize = standardize
if np.any(self._endog_std < 1e-10):
ix = np.where(self._endog_std < 1e-10)
names = np.array(endog_names)[ix[0]].tolist()
raise ValueError('Constant variable(s) found in observed'
' variables, but constants cannot be included'
f' in this model. These variables are: {names}.')
if self.standardize:
endog = (endog - self._endog_mean) / self._endog_std
# Observation / states slices
o = self._o = {
'M': np.s_[:self.k_endog_M],
'Q': np.s_[self.k_endog_M:]}
# Construct the basic state space representation
super().__init__(endog, k_states=s.k_states, k_posdef=s.k_posdef,
**kwargs)
# Revert the standardization for orig_endog
if self.standardize:
self.data.orig_endog = (
self.data.orig_endog * self._endog_std + self._endog_mean)
# State initialization
# Note: we could just initialize the entire thing as stationary, but
# doing each block separately should be faster and avoid numerical
# issues
if 'initialization' not in kwargs:
self.ssm.initialize(self._default_initialization())
# Fixed components of the state space representation
# > design
if self.idiosyncratic_ar1:
self['design', o['M'], s['idio_ar_M']] = np.eye(self.k_endog_M)
multipliers = [1, 2, 3, 2, 1]
for i in range(len(multipliers)):
m = multipliers[i]
self['design', o['Q'], s['idio_ar_Q_ix'][:, i]] = (
m * np.eye(self.k_endog_Q))
# > obs cov
if self.obs_cov_diag:
self['obs_cov'] = np.eye(self.k_endog) * 1e-4
# > transition
for block in s.factor_blocks:
if block.k_factors == 1:
tmp = 0
else:
tmp = np.zeros((block.k_factors, block.k_factors))
self['transition', block['factors'], block['factors']] = (
companion_matrix([1] + [tmp] * block._factor_order).T)
if self.k_endog_Q == 1:
tmp = 0
else:
tmp = np.zeros((self.k_endog_Q, self.k_endog_Q))
self['transition', s['idio_ar_Q'], s['idio_ar_Q']] = (
companion_matrix([1] + [tmp] * 5).T)
# > selection
ix1 = ix2 = 0
for block in s.factor_blocks:
ix2 += block.k_factors
self['selection', block['factors_ix'][:, 0], ix1:ix2] = (
np.eye(block.k_factors))
ix1 = ix2
if self.idiosyncratic_ar1:
ix2 = ix1 + self.k_endog_M
self['selection', s['idio_ar_M'], ix1:ix2] = np.eye(self.k_endog_M)
ix1 = ix2
ix2 = ix1 + self.k_endog_Q
self['selection', s['idio_ar_Q_ix'][:, 0], ix1:ix2] = (
np.eye(self.k_endog_Q))
# Parameters
self.params = OrderedDict([
('loadings', np.sum(self.endog_factor_map.values)),
('factor_ar', np.sum([block.k_factors**2 * block.factor_order
for block in s.factor_blocks])),
('factor_cov', np.sum([block.k_factors * (block.k_factors + 1) // 2
for block in s.factor_blocks])),
('idiosyncratic_ar1',
self.k_endog if self.idiosyncratic_ar1 else 0),
('idiosyncratic_var', self.k_endog)])
self.k_params = np.sum(list(self.params.values()))
# Parameter slices
ix = np.split(np.arange(self.k_params),
np.cumsum(list(self.params.values()))[:-1])
self._p = dict(zip(self.params.keys(), ix))
# Cache
self._loading_constraints = {}
# Initialization kwarg keys, e.g. for cloning
self._init_keys += [
'factors', 'factor_orders', 'factor_multiplicities',
'idiosyncratic_ar1', 'standardize', 'init_t0',
'obs_cov_diag'] + list(kwargs.keys())
[docs]
@classmethod
def construct_endog(cls, endog_monthly, endog_quarterly):
"""
Construct a combined dataset from separate monthly and quarterly data.
Parameters
----------
endog_monthly : array_like
Monthly dataset. If a quarterly dataset is given, then this must
be a Pandas object with a PeriodIndex or DatetimeIndex at a monthly
frequency.
endog_quarterly : array_like or None
Quarterly dataset. If not None, then this must be a Pandas object
with a PeriodIndex or DatetimeIndex at a quarterly frequency.
Returns
-------
endog : array_like
If both endog_monthly and endog_quarterly were given, this is a
Pandas DataFrame with a PeriodIndex at the monthly frequency, with
all of the columns from `endog_monthly` ordered first and the
columns from `endog_quarterly` ordered afterwards. Otherwise it is
simply the input `endog_monthly` dataset.
k_endog_monthly : int
The number of monthly variables (which are ordered first) in the
returned `endog` dataset.
"""
# Create combined dataset
if endog_quarterly is not None:
# Validate endog_monthly
base_msg = ('If given both monthly and quarterly data'
' then the monthly dataset must be a Pandas'
' object with a date index at a monthly frequency.')
if not isinstance(endog_monthly, (pd.Series, pd.DataFrame)):
raise ValueError('Given monthly dataset is not a'
' Pandas object. ' + base_msg)
elif endog_monthly.index.inferred_type not in ("datetime64",
"period"):
raise ValueError('Given monthly dataset has an'
' index with non-date values. ' + base_msg)
elif not getattr(endog_monthly.index, 'freqstr', 'N')[0] == 'M':
freqstr = getattr(endog_monthly.index, 'freqstr', 'None')
raise ValueError('Index of given monthly dataset has a'
' non-monthly frequency (to check this,'
' examine the `freqstr` attribute of the'
' index of the dataset - it should start with'
' M if it is monthly).'
f' Got {freqstr}. ' + base_msg)
# Validate endog_quarterly
base_msg = ('If a quarterly dataset is given, then it must be a'
' Pandas object with a date index at a quarterly'
' frequency.')
if not isinstance(endog_quarterly, (pd.Series, pd.DataFrame)):
raise ValueError('Given quarterly dataset is not a'
' Pandas object. ' + base_msg)
elif endog_quarterly.index.inferred_type not in ("datetime64",
"period"):
raise ValueError('Given quarterly dataset has an'
' index with non-date values. ' + base_msg)
elif not getattr(endog_quarterly.index, 'freqstr', 'N')[0] == 'Q':
freqstr = getattr(endog_quarterly.index, 'freqstr', 'None')
raise ValueError('Index of given quarterly dataset'
' has a non-quarterly frequency (to check'
' this, examine the `freqstr` attribute of'
' the index of the dataset - it should start'
' with Q if it is quarterly).'
f' Got {freqstr}. ' + base_msg)
# Convert to PeriodIndex, if applicable
if hasattr(endog_monthly.index, 'to_period'):
endog_monthly = endog_monthly.to_period('M')
if hasattr(endog_quarterly.index, 'to_period'):
endog_quarterly = endog_quarterly.to_period('Q')
# Combine the datasets
quarterly_resamp = endog_quarterly.copy()
quarterly_resamp.index = quarterly_resamp.index.to_timestamp()
quarterly_resamp = quarterly_resamp.resample(QUARTER_END).first()
quarterly_resamp = quarterly_resamp.resample(MONTH_END).first()
quarterly_resamp.index = quarterly_resamp.index.to_period()
endog = pd.concat([endog_monthly, quarterly_resamp], axis=1)
# Make sure we didn't accidentally get duplicate column names
column_counts = endog.columns.value_counts()
if column_counts.max() > 1:
columns = endog.columns.values.astype(object)
for name in column_counts.index:
count = column_counts.loc[name]
if count == 1:
continue
mask = columns == name
columns[mask] = [f'{name}{i + 1}' for i in range(count)]
endog.columns = columns
else:
endog = endog_monthly.copy()
shape = endog_monthly.shape
k_endog_monthly = shape[1] if len(shape) == 2 else 1
return endog, k_endog_monthly
[docs]
def clone(self, endog, k_endog_monthly=None, endog_quarterly=None,
retain_standardization=False, **kwargs):
"""
Clone state space model with new data and optionally new specification.
Parameters
----------
endog : array_like
The observed time-series process :math:`y`
k_endog_monthly : int, optional
If specifying a monthly/quarterly mixed frequency model in which
the provided `endog` dataset contains both the monthly and
quarterly data, this variable should be used to indicate how many
of the variables are monthly.
endog_quarterly : array_like, optional
Observations of quarterly variables. If provided, must be a
Pandas Series or DataFrame with a DatetimeIndex or PeriodIndex at
the quarterly frequency.
kwargs
Keyword arguments to pass to the new model class to change the
model specification.
Returns
-------
model : DynamicFactorMQ instance
"""
if retain_standardization and self.standardize:
kwargs['standardize'] = (self._endog_mean, self._endog_std)
mod = self._clone_from_init_kwds(
endog, k_endog_monthly=k_endog_monthly,
endog_quarterly=endog_quarterly, **kwargs)
return mod
@property
def _res_classes(self):
return {'fit': (DynamicFactorMQResults, mlemodel.MLEResultsWrapper)}
def _default_initialization(self):
s = self._s
init = initialization.Initialization(self.k_states)
for block in s.factor_blocks:
init.set(block['factors'], 'stationary')
if self.idiosyncratic_ar1:
for i in range(s['idio_ar_M'].start, s['idio_ar_M'].stop):
init.set(i, 'stationary')
init.set(s['idio_ar_Q'], 'stationary')
return init
def _get_endog_names(self, truncate=None, as_string=None):
if truncate is None:
truncate = False if as_string is False or self.k_endog == 1 else 24
if as_string is False and truncate is not False:
raise ValueError('Can only truncate endog names if they'
' are returned as a string.')
if as_string is None:
as_string = truncate is not False
# The base `endog_names` property is only a list if there are at least
# two variables; often, we need it to be a list
endog_names = self.endog_names
if not isinstance(endog_names, list):
endog_names = [endog_names]
if as_string:
endog_names = [str(name) for name in endog_names]
if truncate is not False:
n = truncate
endog_names = [name if len(name) <= n else name[:n] + '...'
for name in endog_names]
return endog_names
@property
def _model_name(self):
model_name = [
'Dynamic Factor Model',
f'{self.k_factors} factors in {self.k_factor_blocks} blocks']
if self.k_endog_Q > 0:
model_name.append('Mixed frequency (M/Q)')
error_type = 'AR(1)' if self.idiosyncratic_ar1 else 'iid'
model_name.append(f'{error_type} idiosyncratic')
return model_name
[docs]
def summary(self, truncate_endog_names=None):
"""
Create a summary table describing the model.
Parameters
----------
truncate_endog_names : int, optional
The number of characters to show for names of observed variables.
Default is 24 if there is more than one observed variable, or
an unlimited number of there is only one.
"""
# Get endog names
endog_names = self._get_endog_names(truncate=truncate_endog_names,
as_string=True)
title = 'Model Specification: Dynamic Factor Model'
if self._index_dates:
ix = self._index
d = ix[0]
sample = ['%s' % d]
d = ix[-1]
sample += ['- ' + '%s' % d]
else:
sample = [str(0), ' - ' + str(self.nobs)]
# Standardize the model name as a list of str
model_name = self._model_name
# - Top summary table ------------------------------------------------
top_left = []
top_left.append(('Model:', [model_name[0]]))
for i in range(1, len(model_name)):
top_left.append(('', ['+ ' + model_name[i]]))
top_left += [
('Sample:', [sample[0]]),
('', [sample[1]])]
top_right = []
if self.k_endog_Q > 0:
top_right += [
('# of monthly variables:', [self.k_endog_M]),
('# of quarterly variables:', [self.k_endog_Q])]
else:
top_right += [('# of observed variables:', [self.k_endog])]
if self.k_factor_blocks == 1:
top_right += [('# of factors:', [self.k_factors])]
else:
top_right += [('# of factor blocks:', [self.k_factor_blocks])]
top_right += [('Idiosyncratic disturbances:',
['AR(1)' if self.idiosyncratic_ar1 else 'iid']),
('Standardize variables:', [self.standardize])]
summary = Summary()
self.model = self
summary.add_table_2cols(self, gleft=top_left, gright=top_right,
title=title)
table_ix = 1
del self.model
# - Endog / factor map -----------------------------------------------
data = self.endog_factor_map.replace({True: 'X', False: ''})
data.index = endog_names
try:
items = data.items()
except AttributeError:
# Remove after pandas 1.5 is minimum
items = data.iteritems()
for name, col in items:
data[name] = data[name] + (' ' * (len(name) // 2))
data.index.name = 'Dep. variable'
data = data.reset_index()
params_data = data.values
params_header = data.columns.map(str).tolist()
params_stubs = None
title = 'Observed variables / factor loadings'
table = SimpleTable(
params_data, params_header, params_stubs,
txt_fmt=fmt_params, title=title)
summary.tables.insert(table_ix, table)
table_ix += 1
# - Factor blocks summary table --------------------------------------
data = self.factor_block_orders.reset_index()
data['block'] = data['block'].map(
lambda factor_names: ', '.join(factor_names))
try:
data[['order']] = data[['order']].map(str)
except AttributeError:
data[['order']] = data[['order']].applymap(str)
params_data = data.values
params_header = data.columns.map(str).tolist()
params_stubs = None
title = 'Factor blocks:'
table = SimpleTable(
params_data, params_header, params_stubs,
txt_fmt=fmt_params, title=title)
summary.tables.insert(table_ix, table)
table_ix += 1
return summary
def __str__(self):
"""Summary tables showing model specification."""
return str(self.summary())
@property
def state_names(self):
"""(list of str) List of human readable names for unobserved states."""
# Factors
state_names = []
for block in self._s.factor_blocks:
state_names += [f'{name}' for name in block.factor_names[:]]
for s in range(1, block._factor_order):
state_names += [f'L{s}.{name}'
for name in block.factor_names]
# Monthly error
endog_names = self._get_endog_names()
if self.idiosyncratic_ar1:
endog_names_M = endog_names[self._o['M']]
state_names += [f'eps_M.{name}' for name in endog_names_M]
endog_names_Q = endog_names[self._o['Q']]
# Quarterly error
state_names += [f'eps_Q.{name}' for name in endog_names_Q]
for s in range(1, 5):
state_names += [f'L{s}.eps_Q.{name}' for name in endog_names_Q]
return state_names
@property
def param_names(self):
"""(list of str) List of human readable parameter names."""
param_names = []
# Loadings
# So that Lambda = params[ix].reshape(self.k_endog, self.k_factors)
# (where Lambda stacks Lambda_M and Lambda_Q)
endog_names = self._get_endog_names(as_string=False)
for endog_name in endog_names:
for block in self._s.factor_blocks:
for factor_name in block.factor_names:
if self.endog_factor_map.loc[endog_name, factor_name]:
param_names.append(
f'loading.{factor_name}->{endog_name}')
# Factor VAR
for block in self._s.factor_blocks:
for to_factor in block.factor_names:
param_names += [f'L{i}.{from_factor}->{to_factor}'
for i in range(1, block.factor_order + 1)
for from_factor in block.factor_names]
# Factor covariance
for i in range(len(self._s.factor_blocks)):
block = self._s.factor_blocks[i]
param_names += [f'fb({i}).cov.chol[{j + 1},{k + 1}]'
for j in range(block.k_factors)
for k in range(j + 1)]
# Error AR(1)
if self.idiosyncratic_ar1:
endog_names_M = endog_names[self._o['M']]
param_names += [f'L1.eps_M.{name}' for name in endog_names_M]
endog_names_Q = endog_names[self._o['Q']]
param_names += [f'L1.eps_Q.{name}' for name in endog_names_Q]
# Error innovation variances
param_names += [f'sigma2.{name}' for name in endog_names]
return param_names
@property
def start_params(self):
"""(array) Starting parameters for maximum likelihood estimation."""
params = np.zeros(self.k_params, dtype=np.float64)
# (1) estimate factors one at a time, where the first step uses
# PCA on all `endog` variables that load on the first factor, and
# subsequent steps use residuals from the previous steps.
# TODO: what about factors that only load on quarterly variables?
endog_factor_map_M = self.endog_factor_map.iloc[:self.k_endog_M]
factors = []
endog = np.require(
pd.DataFrame(self.endog).interpolate().bfill(),
requirements="W"
)
for name in self.factor_names:
# Try to retrieve this from monthly variables, which is most
# consistent
endog_ix = np.where(endog_factor_map_M.loc[:, name])[0]
# But fall back to quarterly if necessary
if len(endog_ix) == 0:
endog_ix = np.where(self.endog_factor_map.loc[:, name])[0]
factor_endog = endog[:, endog_ix]
res_pca = PCA(factor_endog, ncomp=1, method='eig', normalize=False)
factors.append(res_pca.factors)
endog[:, endog_ix] -= res_pca.projection
factors = np.concatenate(factors, axis=1)
# (2) Estimate coefficients for each endog, one at a time (OLS for
# monthly variables, restricted OLS for quarterly). Also, compute
# residuals.
loadings = []
resid = []
for i in range(self.k_endog_M):
factor_ix = self._s.endog_factor_iloc[i]
factor_exog = factors[:, factor_ix]
mod_ols = OLS(self.endog[:, i], exog=factor_exog, missing='drop')
res_ols = mod_ols.fit()
loadings += res_ols.params.tolist()
resid.append(res_ols.resid)
for i in range(self.k_endog_M, self.k_endog):
factor_ix = self._s.endog_factor_iloc[i]
factor_exog = lagmat(factors[:, factor_ix], 4, original='in')
mod_glm = GLM(self.endog[:, i], factor_exog, missing='drop')
res_glm = mod_glm.fit_constrained(self.loading_constraints(i))
loadings += res_glm.params[:len(factor_ix)].tolist()
resid.append(res_glm.resid_response)
params[self._p['loadings']] = loadings
# (3) For each factor block, use an AR or VAR model to get coefficients
# and covariance estimate
# Factor transitions
stationary = True
factor_ar = []
factor_cov = []
i = 0
for block in self._s.factor_blocks:
factors_endog = factors[:, i:i + block.k_factors]
i += block.k_factors
if block.factor_order == 0:
continue
if block.k_factors == 1:
mod_factors = SARIMAX(factors_endog,
order=(block.factor_order, 0, 0))
sp = mod_factors.start_params
block_factor_ar = sp[:-1]
block_factor_cov = sp[-1:]
coefficient_matrices = mod_factors.start_params[:-1]
elif block.k_factors > 1:
mod_factors = VAR(factors_endog)
res_factors = mod_factors.fit(
maxlags=block.factor_order, ic=None, trend='n')
block_factor_ar = res_factors.params.T.ravel()
L = np.linalg.cholesky(res_factors.sigma_u)
block_factor_cov = L[np.tril_indices_from(L)]
coefficient_matrices = np.transpose(
np.reshape(block_factor_ar,
(block.k_factors, block.k_factors,
block.factor_order)), (2, 0, 1))
# Test for stationarity
stationary = is_invertible([1] + list(-coefficient_matrices))
# Check for stationarity
if not stationary:
warn('Non-stationary starting factor autoregressive'
' parameters found for factor block'
f' {block.factor_names}. Using zeros as starting'
' parameters.')
block_factor_ar[:] = 0
cov_factor = np.diag(factors_endog.std(axis=0))
block_factor_cov = (
cov_factor[np.tril_indices(block.k_factors)])
factor_ar += block_factor_ar.tolist()
factor_cov += block_factor_cov.tolist()
params[self._p['factor_ar']] = factor_ar
params[self._p['factor_cov']] = factor_cov
# (4) Use residuals from step (2) to estimate the idiosyncratic
# component
# Idiosyncratic component
if self.idiosyncratic_ar1:
idio_ar1 = []
idio_var = []
for i in range(self.k_endog_M):
mod_idio = SARIMAX(resid[i], order=(1, 0, 0), trend='c')
sp = mod_idio.start_params
idio_ar1.append(np.clip(sp[1], -0.99, 0.99))
idio_var.append(np.clip(sp[-1], 1e-5, np.inf))
for i in range(self.k_endog_M, self.k_endog):
y = self.endog[:, i].copy()
y[~np.isnan(y)] = resid[i]
mod_idio = QuarterlyAR1(y)
res_idio = mod_idio.fit(maxiter=10, return_params=True,
disp=False)
res_idio = mod_idio.fit_em(res_idio, maxiter=5,
return_params=True)
idio_ar1.append(np.clip(res_idio[0], -0.99, 0.99))
idio_var.append(np.clip(res_idio[1], 1e-5, np.inf))
params[self._p['idiosyncratic_ar1']] = idio_ar1
params[self._p['idiosyncratic_var']] = idio_var
else:
idio_var = [np.var(resid[i]) for i in range(self.k_endog_M)]
for i in range(self.k_endog_M, self.k_endog):
y = self.endog[:, i].copy()
y[~np.isnan(y)] = resid[i]
mod_idio = QuarterlyAR1(y)
res_idio = mod_idio.fit(return_params=True, disp=False)
idio_var.append(np.clip(res_idio[1], 1e-5, np.inf))
params[self._p['idiosyncratic_var']] = idio_var
return params
[docs]
def transform_params(self, unconstrained):
"""
Transform parameters from optimizer space to model space.
Transform unconstrained parameters used by the optimizer to constrained
parameters used in likelihood evaluation.
Parameters
----------
unconstrained : array_like
Array of unconstrained parameters used by the optimizer, to be
transformed.
Returns
-------
constrained : array_like
Array of constrained parameters which may be used in likelihood
evaluation.
"""
constrained = unconstrained.copy()
# Stationary factor VAR
unconstrained_factor_ar = unconstrained[self._p['factor_ar']]
constrained_factor_ar = []
i = 0
for block in self._s.factor_blocks:
length = block.k_factors**2 * block.factor_order
tmp_coeff = np.reshape(
unconstrained_factor_ar[i:i + length],
(block.k_factors, block.k_factors * block.factor_order))
tmp_cov = np.eye(block.k_factors)
tmp_coeff, _ = constrain_stationary_multivariate(tmp_coeff,
tmp_cov)
constrained_factor_ar += tmp_coeff.ravel().tolist()
i += length
constrained[self._p['factor_ar']] = constrained_factor_ar
# Stationary idiosyncratic AR(1)
if self.idiosyncratic_ar1:
idio_ar1 = unconstrained[self._p['idiosyncratic_ar1']]
constrained[self._p['idiosyncratic_ar1']] = [
constrain_stationary_univariate(idio_ar1[i:i + 1])[0]
for i in range(self.k_endog)]
# Positive idiosyncratic variances
constrained[self._p['idiosyncratic_var']] = (
constrained[self._p['idiosyncratic_var']]**2)
return constrained
[docs]
def untransform_params(self, constrained):
"""
Transform parameters from model space to optimizer space.
Transform constrained parameters used in likelihood evaluation
to unconstrained parameters used by the optimizer.
Parameters
----------
constrained : array_like
Array of constrained parameters used in likelihood evaluation, to
be transformed.
Returns
-------
unconstrained : array_like
Array of unconstrained parameters used by the optimizer.
"""
unconstrained = constrained.copy()
# Stationary factor VAR
constrained_factor_ar = constrained[self._p['factor_ar']]
unconstrained_factor_ar = []
i = 0
for block in self._s.factor_blocks:
length = block.k_factors**2 * block.factor_order
tmp_coeff = np.reshape(
constrained_factor_ar[i:i + length],
(block.k_factors, block.k_factors * block.factor_order))
tmp_cov = np.eye(block.k_factors)
tmp_coeff, _ = unconstrain_stationary_multivariate(tmp_coeff,
tmp_cov)
unconstrained_factor_ar += tmp_coeff.ravel().tolist()
i += length
unconstrained[self._p['factor_ar']] = unconstrained_factor_ar
# Stationary idiosyncratic AR(1)
if self.idiosyncratic_ar1:
idio_ar1 = constrained[self._p['idiosyncratic_ar1']]
unconstrained[self._p['idiosyncratic_ar1']] = [
unconstrain_stationary_univariate(idio_ar1[i:i + 1])[0]
for i in range(self.k_endog)]
# Positive idiosyncratic variances
unconstrained[self._p['idiosyncratic_var']] = (
unconstrained[self._p['idiosyncratic_var']]**0.5)
return unconstrained
[docs]
def update(self, params, **kwargs):
"""
Update the parameters of the model.
Parameters
----------
params : array_like
Array of new parameters.
transformed : bool, optional
Whether or not `params` is already transformed. If set to False,
`transform_params` is called. Default is True.
"""
params = super().update(params, **kwargs)
# Local copies
o = self._o
s = self._s
p = self._p
# Loadings
loadings = params[p['loadings']]
start = 0
for i in range(self.k_endog_M):
iloc = self._s.endog_factor_iloc[i]
k_factors = len(iloc)
factor_ix = s['factors_L1'][iloc]
self['design', i, factor_ix] = loadings[start:start + k_factors]
start += k_factors
multipliers = np.array([1, 2, 3, 2, 1])[:, None]
for i in range(self.k_endog_M, self.k_endog):
iloc = self._s.endog_factor_iloc[i]
k_factors = len(iloc)
factor_ix = s['factors_L1_5_ix'][:, iloc]
self['design', i, factor_ix.ravel()] = np.ravel(
loadings[start:start + k_factors] * multipliers)
start += k_factors
# Factor VAR
factor_ar = params[p['factor_ar']]
start = 0
for block in s.factor_blocks:
k_params = block.k_factors**2 * block.factor_order
A = np.reshape(
factor_ar[start:start + k_params],
(block.k_factors, block.k_factors * block.factor_order))
start += k_params
self['transition', block['factors_L1'], block['factors_ar']] = A
# Factor covariance
factor_cov = params[p['factor_cov']]
start = 0
ix1 = 0
for block in s.factor_blocks:
k_params = block.k_factors * (block.k_factors + 1) // 2
L = np.zeros((block.k_factors, block.k_factors),
dtype=params.dtype)
L[np.tril_indices_from(L)] = factor_cov[start:start + k_params]
start += k_params
Q = L @ L.T
ix2 = ix1 + block.k_factors
self['state_cov', ix1:ix2, ix1:ix2] = Q
ix1 = ix2
# Error AR(1)
if self.idiosyncratic_ar1:
alpha = np.diag(params[p['idiosyncratic_ar1']])
self['transition', s['idio_ar_L1'], s['idio_ar_L1']] = alpha
# Error variances
if self.idiosyncratic_ar1:
self['state_cov', self.k_factors:, self.k_factors:] = (
np.diag(params[p['idiosyncratic_var']]))
else:
idio_var = params[p['idiosyncratic_var']]
self['obs_cov', o['M'], o['M']] = np.diag(idio_var[o['M']])
self['state_cov', self.k_factors:, self.k_factors:] = (
np.diag(idio_var[o['Q']]))
@property
def loglike_constant(self):
"""
Constant term in the joint log-likelihood function.
Useful in facilitating comparisons to other packages that exclude the
constant from the log-likelihood computation.
"""
return -0.5 * (1 - np.isnan(self.endog)).sum() * np.log(2 * np.pi)
[docs]
def loading_constraints(self, i):
r"""
Matrix formulation of quarterly variables' factor loading constraints.
Parameters
----------
i : int
Index of the `endog` variable to compute constraints for.
Returns
-------
R : array (k_constraints, k_factors * 5)
q : array (k_constraints,)
Notes
-----
If the factors were known, then the factor loadings for the ith
quarterly variable would be computed by a linear regression of the form
y_i = A_i' f + B_i' L1.f + C_i' L2.f + D_i' L3.f + E_i' L4.f
where:
- f is (k_i x 1) and collects all of the factors that load on y_i
- L{j}.f is (k_i x 1) and collects the jth lag of each factor
- A_i, ..., E_i are (k_i x 1) and collect factor loadings
As the observed variable is quarterly while the factors are monthly, we
want to restrict the estimated regression coefficients to be:
y_i = A_i f + 2 A_i L1.f + 3 A_i L2.f + 2 A_i L3.f + A_i L4.f
Stack the unconstrained coefficients: \Lambda_i = [A_i' B_i' ... E_i']'
Then the constraints can be written as follows, for l = 1, ..., k_i
- 2 A_{i,l} - B_{i,l} = 0
- 3 A_{i,l} - C_{i,l} = 0
- 2 A_{i,l} - D_{i,l} = 0
- A_{i,l} - E_{i,l} = 0
So that k_constraints = 4 * k_i. In matrix form the constraints are:
.. math::
R \Lambda_i = q
where :math:`\Lambda_i` is shaped `(k_i * 5,)`, :math:`R` is shaped
`(k_constraints, k_i * 5)`, and :math:`q` is shaped `(k_constraints,)`.
For example, for the case that k_i = 2, we can write:
| 2 0 -1 0 0 0 0 0 0 0 | | A_{i,1} | | 0 |
| 0 2 0 -1 0 0 0 0 0 0 | | A_{i,2} | | 0 |
| 3 0 0 0 -1 0 0 0 0 0 | | B_{i,1} | | 0 |
| 0 3 0 0 0 -1 0 0 0 0 | | B_{i,2} | | 0 |
| 2 0 0 0 0 0 -1 0 0 0 | | C_{i,1} | = | 0 |
| 0 2 0 0 0 0 0 -1 0 0 | | C_{i,2} | | 0 |
| 1 0 0 0 0 0 0 0 -1 0 | | D_{i,1} | | 0 |
| 0 1 0 0 0 0 0 0 0 -1 | | D_{i,2} | | 0 |
| E_{i,1} | | 0 |
| E_{i,2} | | 0 |
"""
if i < self.k_endog_M:
raise ValueError('No constraints for monthly variables.')
if i not in self._loading_constraints:
k_factors = self.endog_factor_map.iloc[i].sum()
R = np.zeros((k_factors * 4, k_factors * 5))
q = np.zeros(R.shape[0])
# Let R = [R_1 R_2]
# Then R_1 is multiples of the identity matrix
multipliers = np.array([1, 2, 3, 2, 1])
R[:, :k_factors] = np.reshape(
(multipliers[1:] * np.eye(k_factors)[..., None]).T,
(k_factors * 4, k_factors))
# And R_2 is the identity
R[:, k_factors:] = np.diag([-1] * (k_factors * 4))
self._loading_constraints[i] = (R, q)
return self._loading_constraints[i]
[docs]
def fit(self, start_params=None, transformed=True, includes_fixed=False,
cov_type='none', cov_kwds=None, method='em', maxiter=500,
tolerance=1e-6, em_initialization=True, mstep_method=None,
full_output=1, disp=False, callback=None, return_params=False,
optim_score=None, optim_complex_step=None, optim_hessian=None,
flags=None, low_memory=False, llf_decrease_action='revert',
llf_decrease_tolerance=1e-4, **kwargs):
"""
Fits the model by maximum likelihood via Kalman filter.
Parameters
----------
start_params : array_like, optional
Initial guess of the solution for the loglikelihood maximization.
If None, the default is given by Model.start_params.
transformed : bool, optional
Whether or not `start_params` is already transformed. Default is
True.
includes_fixed : bool, optional
If parameters were previously fixed with the `fix_params` method,
this argument describes whether or not `start_params` also includes
the fixed parameters, in addition to the free parameters. Default
is False.
cov_type : str, optional
The `cov_type` keyword governs the method for calculating the
covariance matrix of parameter estimates. Can be one of:
- 'opg' for the outer product of gradient estimator
- 'oim' for the observed information matrix estimator, calculated
using the method of Harvey (1989)
- 'approx' for the observed information matrix estimator,
calculated using a numerical approximation of the Hessian matrix.
- 'robust' for an approximate (quasi-maximum likelihood) covariance
matrix that may be valid even in the presence of some
misspecifications. Intermediate calculations use the 'oim'
method.
- 'robust_approx' is the same as 'robust' except that the
intermediate calculations use the 'approx' method.
- 'none' for no covariance matrix calculation.
Default is 'none', since computing this matrix can be very slow
when there are a large number of parameters.
cov_kwds : dict or None, optional
A dictionary of arguments affecting covariance matrix computation.
**opg, oim, approx, robust, robust_approx**
- 'approx_complex_step' : bool, optional - If True, numerical
approximations are computed using complex-step methods. If False,
numerical approximations are computed using finite difference
methods. Default is True.
- 'approx_centered' : bool, optional - If True, numerical
approximations computed using finite difference methods use a
centered approximation. Default is False.
method : str, optional
The `method` determines which solver from `scipy.optimize`
is used, and it can be chosen from among the following strings:
- 'em' for the EM algorithm
- 'newton' for Newton-Raphson
- 'nm' for Nelder-Mead
- 'bfgs' for Broyden-Fletcher-Goldfarb-Shanno (BFGS)
- 'lbfgs' for limited-memory BFGS with optional box constraints
- 'powell' for modified Powell's method
- 'cg' for conjugate gradient
- 'ncg' for Newton-conjugate gradient
- 'basinhopping' for global basin-hopping solver
The explicit arguments in `fit` are passed to the solver,
with the exception of the basin-hopping solver. Each
solver has several optional arguments that are not the same across
solvers. See the notes section below (or scipy.optimize) for the
available arguments and for the list of explicit arguments that the
basin-hopping solver supports.
maxiter : int, optional
The maximum number of iterations to perform.
tolerance : float, optional
Tolerance to use for convergence checking when using the EM
algorithm. To set the tolerance for other methods, pass
the optimizer-specific keyword argument(s).
full_output : bool, optional
Set to True to have all available output in the Results object's
mle_retvals attribute. The output is dependent on the solver.
See LikelihoodModelResults notes section for more information.
disp : bool, optional
Set to True to print convergence messages.
callback : callable callback(xk), optional
Called after each iteration, as callback(xk), where xk is the
current parameter vector.
return_params : bool, optional
Whether or not to return only the array of maximizing parameters.
Default is False.
optim_score : {'harvey', 'approx'} or None, optional
The method by which the score vector is calculated. 'harvey' uses
the method from Harvey (1989), 'approx' uses either finite
difference or complex step differentiation depending upon the
value of `optim_complex_step`, and None uses the built-in gradient
approximation of the optimizer. Default is None. This keyword is
only relevant if the optimization method uses the score.
optim_complex_step : bool, optional
Whether or not to use complex step differentiation when
approximating the score; if False, finite difference approximation
is used. Default is True. This keyword is only relevant if
`optim_score` is set to 'harvey' or 'approx'.
optim_hessian : {'opg','oim','approx'}, optional
The method by which the Hessian is numerically approximated. 'opg'
uses outer product of gradients, 'oim' uses the information
matrix formula from Harvey (1989), and 'approx' uses numerical
approximation. This keyword is only relevant if the
optimization method uses the Hessian matrix.
low_memory : bool, optional
If set to True, techniques are applied to substantially reduce
memory usage. If used, some features of the results object will
not be available (including smoothed results and in-sample
prediction), although out-of-sample forecasting is possible.
Note that this option is not available when using the EM algorithm
(which is the default for this model). Default is False.
llf_decrease_action : {'ignore', 'warn', 'revert'}, optional
Action to take if the log-likelihood decreases in an EM iteration.
'ignore' continues the iterations, 'warn' issues a warning but
continues the iterations, while 'revert' ends the iterations and
returns the result from the last good iteration. Default is 'warn'.
llf_decrease_tolerance : float, optional
Minimum size of the log-likelihood decrease required to trigger a
warning or to end the EM iterations. Setting this value slightly
larger than zero allows small decreases in the log-likelihood that
may be caused by numerical issues. If set to zero, then any
decrease will trigger the `llf_decrease_action`. Default is 1e-4.
**kwargs
Additional keyword arguments to pass to the optimizer.
Returns
-------
MLEResults
See Also
--------
statsmodels.base.model.LikelihoodModel.fit
statsmodels.tsa.statespace.mlemodel.MLEResults
"""
if method == 'em':
return self.fit_em(
start_params=start_params, transformed=transformed,
cov_type=cov_type, cov_kwds=cov_kwds, maxiter=maxiter,
tolerance=tolerance, em_initialization=em_initialization,
mstep_method=mstep_method, full_output=full_output, disp=disp,
return_params=return_params, low_memory=low_memory,
llf_decrease_action=llf_decrease_action,
llf_decrease_tolerance=llf_decrease_tolerance, **kwargs)
else:
return super().fit(
start_params=start_params, transformed=transformed,
includes_fixed=includes_fixed, cov_type=cov_type,
cov_kwds=cov_kwds, method=method, maxiter=maxiter,
full_output=full_output, disp=disp,
callback=callback, return_params=return_params,
optim_score=optim_score,
optim_complex_step=optim_complex_step,
optim_hessian=optim_hessian, flags=flags,
low_memory=low_memory, **kwargs)
[docs]
def fit_em(self, start_params=None, transformed=True, cov_type='none',
cov_kwds=None, maxiter=500, tolerance=1e-6, disp=False,
em_initialization=True, mstep_method=None, full_output=True,
return_params=False, low_memory=False,
llf_decrease_action='revert', llf_decrease_tolerance=1e-4):
"""
Fits the model by maximum likelihood via the EM algorithm.
Parameters
----------
start_params : array_like, optional
Initial guess of the solution for the loglikelihood maximization.
The default is to use `DynamicFactorMQ.start_params`.
transformed : bool, optional
Whether or not `start_params` is already transformed. Default is
True.
cov_type : str, optional
The `cov_type` keyword governs the method for calculating the
covariance matrix of parameter estimates. Can be one of:
- 'opg' for the outer product of gradient estimator
- 'oim' for the observed information matrix estimator, calculated
using the method of Harvey (1989)
- 'approx' for the observed information matrix estimator,
calculated using a numerical approximation of the Hessian matrix.
- 'robust' for an approximate (quasi-maximum likelihood) covariance
matrix that may be valid even in the presence of some
misspecifications. Intermediate calculations use the 'oim'
method.
- 'robust_approx' is the same as 'robust' except that the
intermediate calculations use the 'approx' method.
- 'none' for no covariance matrix calculation.
Default is 'none', since computing this matrix can be very slow
when there are a large number of parameters.
cov_kwds : dict or None, optional
A dictionary of arguments affecting covariance matrix computation.
**opg, oim, approx, robust, robust_approx**
- 'approx_complex_step' : bool, optional - If True, numerical
approximations are computed using complex-step methods. If False,
numerical approximations are computed using finite difference
methods. Default is True.
- 'approx_centered' : bool, optional - If True, numerical
approximations computed using finite difference methods use a
centered approximation. Default is False.
maxiter : int, optional
The maximum number of EM iterations to perform.
tolerance : float, optional
Parameter governing convergence of the EM algorithm. The
`tolerance` is the minimum relative increase in the likelihood
for which convergence will be declared. A smaller value for the
`tolerance` will typically yield more precise parameter estimates,
but will typically require more EM iterations. Default is 1e-6.
disp : int or bool, optional
Controls printing of EM iteration progress. If an integer, progress
is printed at every `disp` iterations. A value of True is
interpreted as the value of 1. Default is False (nothing will be
printed).
em_initialization : bool, optional
Whether or not to also update the Kalman filter initialization
using the EM algorithm. Default is True.
mstep_method : {None, 'missing', 'nonmissing'}, optional
The EM algorithm maximization step. If there are no NaN values
in the dataset, this can be set to "nonmissing" (which is slightly
faster) or "missing", otherwise it must be "missing". Default is
"nonmissing" if there are no NaN values or "missing" if there are.
full_output : bool, optional
Set to True to have all available output from EM iterations in
the Results object's mle_retvals attribute.
return_params : bool, optional
Whether or not to return only the array of maximizing parameters.
Default is False.
low_memory : bool, optional
This option cannot be used with the EM algorithm and will raise an
error if set to True. Default is False.
llf_decrease_action : {'ignore', 'warn', 'revert'}, optional
Action to take if the log-likelihood decreases in an EM iteration.
'ignore' continues the iterations, 'warn' issues a warning but
continues the iterations, while 'revert' ends the iterations and
returns the result from the last good iteration. Default is 'warn'.
llf_decrease_tolerance : float, optional
Minimum size of the log-likelihood decrease required to trigger a
warning or to end the EM iterations. Setting this value slightly
larger than zero allows small decreases in the log-likelihood that
may be caused by numerical issues. If set to zero, then any
decrease will trigger the `llf_decrease_action`. Default is 1e-4.
Returns
-------
DynamicFactorMQResults
See Also
--------
statsmodels.tsa.statespace.mlemodel.MLEModel.fit
statsmodels.tsa.statespace.mlemodel.MLEResults
"""
if self._has_fixed_params:
raise NotImplementedError('Cannot fit using the EM algorithm while'
' holding some parameters fixed.')
if low_memory:
raise ValueError('Cannot fit using the EM algorithm when using'
' low_memory option.')
if start_params is None:
start_params = self.start_params
transformed = True
else:
start_params = np.array(start_params, ndmin=1)
if not transformed:
start_params = self.transform_params(start_params)
llf_decrease_action = string_like(
llf_decrease_action, 'llf_decrease_action',
options=['ignore', 'warn', 'revert'])
disp = int(disp)
# Perform expectation-maximization
s = self._s
llf = []
params = [start_params]
init = None
inits = [self.ssm.initialization]
i = 0
delta = 0
terminate = False
# init_stationary = None if em_initialization else True
while i < maxiter and not terminate and (i < 1 or (delta > tolerance)):
out = self._em_iteration(params[-1], init=init,
mstep_method=mstep_method)
new_llf = out[0].llf_obs.sum()
# If we are not using EM initialization, then we need to check for
# non-stationary parameters
if not em_initialization:
self.update(out[1])
switch_init = []
T = self['transition']
init = self.ssm.initialization
iloc = np.arange(self.k_states)
# We may only have global initialization if we have no
# quarterly variables and idiosyncratic_ar1=False
if self.k_endog_Q == 0 and not self.idiosyncratic_ar1:
block = s.factor_blocks[0]
if init.initialization_type == 'stationary':
Tb = T[block['factors'], block['factors']]
if not np.all(np.linalg.eigvals(Tb) < (1 - 1e-10)):
init.set(block['factors'], 'diffuse')
switch_init.append(
'factor block:'
f' {tuple(block.factor_names)}')
else:
# Factor blocks
for block in s.factor_blocks:
b = tuple(iloc[block['factors']])
init_type = init.blocks[b].initialization_type
if init_type == 'stationary':
Tb = T[block['factors'], block['factors']]
if not np.all(np.linalg.eigvals(Tb) < (1 - 1e-10)):
init.set(block['factors'], 'diffuse')
switch_init.append(
'factor block:'
f' {tuple(block.factor_names)}')
if self.idiosyncratic_ar1:
endog_names = self._get_endog_names(as_string=True)
# Monthly variables
for j in range(s['idio_ar_M'].start, s['idio_ar_M'].stop):
init_type = init.blocks[(j,)].initialization_type
if init_type == 'stationary':
if not np.abs(T[j, j]) < (1 - 1e-10):
init.set(j, 'diffuse')
name = endog_names[j - s['idio_ar_M'].start]
switch_init.append(
'idiosyncratic AR(1) for monthly'
f' variable: {name}')
# Quarterly variables
if self.k_endog_Q > 0:
b = tuple(iloc[s['idio_ar_Q']])
init_type = init.blocks[b].initialization_type
if init_type == 'stationary':
Tb = T[s['idio_ar_Q'], s['idio_ar_Q']]
if not np.all(np.linalg.eigvals(Tb) < (1 - 1e-10)):
init.set(s['idio_ar_Q'], 'diffuse')
switch_init.append(
'idiosyncratic AR(1) for the'
' block of quarterly variables')
if len(switch_init) > 0:
warn('Non-stationary parameters found at EM iteration'
f' {i + 1}, which is not compatible with'
' stationary initialization. Initialization was'
' switched to diffuse for the following: '
f' {switch_init}, and fitting was restarted.')
results = self.fit_em(
start_params=params[-1], transformed=transformed,
cov_type=cov_type, cov_kwds=cov_kwds,
maxiter=maxiter, tolerance=tolerance,
em_initialization=em_initialization,
mstep_method=mstep_method, full_output=full_output,
disp=disp, return_params=return_params,
low_memory=low_memory,
llf_decrease_action=llf_decrease_action,
llf_decrease_tolerance=llf_decrease_tolerance)
self.ssm.initialize(self._default_initialization())
return results
# Check for decrease in the log-likelihood
# Note: allow a little numerical error before declaring a decrease
llf_decrease = (
i > 0 and (new_llf - llf[-1]) < -llf_decrease_tolerance)
if llf_decrease_action == 'revert' and llf_decrease:
warn(f'Log-likelihood decreased at EM iteration {i + 1}.'
f' Reverting to the results from EM iteration {i}'
' (prior to the decrease) and returning the solution.')
# Terminated iteration
i -= 1
terminate = True
else:
if llf_decrease_action == 'warn' and llf_decrease:
warn(f'Log-likelihood decreased at EM iteration {i + 1},'
' which can indicate numerical issues.')
llf.append(new_llf)
params.append(out[1])
if em_initialization:
init = initialization.Initialization(
self.k_states, 'known',
constant=out[0].smoothed_state[..., 0],
stationary_cov=out[0].smoothed_state_cov[..., 0])
inits.append(init)
if i > 0:
delta = (2 * np.abs(llf[-1] - llf[-2]) /
(np.abs(llf[-1]) + np.abs(llf[-2])))
else:
delta = np.inf
# If `disp` is not False, display the first iteration
if disp and i == 0:
print(f'EM start iterations, llf={llf[-1]:.5g}')
# Print output every `disp` observations
elif disp and ((i + 1) % disp) == 0:
print(f'EM iteration {i + 1}, llf={llf[-1]:.5g},'
f' convergence criterion={delta:.5g}')
# Advance the iteration counter
i += 1
# Check for convergence
not_converged = (i == maxiter and delta > tolerance)
# If no convergence without explicit termination, warn users
if not_converged:
warn(f'EM reached maximum number of iterations ({maxiter}),'
f' without achieving convergence: llf={llf[-1]:.5g},'
f' convergence criterion={delta:.5g}'
f' (while specified tolerance was {tolerance:.5g})')
# If `disp` is not False, display the final iteration
if disp:
if terminate:
print(f'EM terminated at iteration {i}, llf={llf[-1]:.5g},'
f' convergence criterion={delta:.5g}'
f' (while specified tolerance was {tolerance:.5g})')
elif not_converged:
print(f'EM reached maximum number of iterations ({maxiter}),'
f' without achieving convergence: llf={llf[-1]:.5g},'
f' convergence criterion={delta:.5g}'
f' (while specified tolerance was {tolerance:.5g})')
else:
print(f'EM converged at iteration {i}, llf={llf[-1]:.5g},'
f' convergence criterion={delta:.5g}'
f' < tolerance={tolerance:.5g}')
# Just return the fitted parameters if requested
if return_params:
result = params[-1]
# Otherwise construct the results class if desired
else:
if em_initialization:
base_init = self.ssm.initialization
self.ssm.initialization = init
# Note that because we are using params[-1], we are actually using
# the results from one additional iteration compared to the
# iteration at which we declared convergence.
result = self.smooth(params[-1], transformed=True,
cov_type=cov_type, cov_kwds=cov_kwds)
if em_initialization:
self.ssm.initialization = base_init
# Save the output
if full_output:
llf.append(result.llf)
em_retvals = Bunch(**{'params': np.array(params),
'llf': np.array(llf),
'iter': i,
'inits': inits})
em_settings = Bunch(**{'method': 'em',
'tolerance': tolerance,
'maxiter': maxiter})
else:
em_retvals = None
em_settings = None
result._results.mle_retvals = em_retvals
result._results.mle_settings = em_settings
return result
def _em_iteration(self, params0, init=None, mstep_method=None):
"""EM iteration."""
# (E)xpectation step
res = self._em_expectation_step(params0, init=init)
# (M)aximization step
params1 = self._em_maximization_step(res, params0,
mstep_method=mstep_method)
return res, params1
def _em_expectation_step(self, params0, init=None):
"""EM expectation step."""
# (E)xpectation step
self.update(params0)
# Re-initialize state, if new initialization is given
if init is not None:
base_init = self.ssm.initialization
self.ssm.initialization = init
# Perform smoothing, only saving what is required
res = self.ssm.smooth(
SMOOTHER_STATE | SMOOTHER_STATE_COV | SMOOTHER_STATE_AUTOCOV,
update_filter=False)
res.llf_obs = np.array(
self.ssm._kalman_filter.loglikelihood, copy=True)
# Reset initialization
if init is not None:
self.ssm.initialization = base_init
return res
def _em_maximization_step(self, res, params0, mstep_method=None):
"""EM maximization step."""
s = self._s
a = res.smoothed_state.T[..., None]
cov_a = res.smoothed_state_cov.transpose(2, 0, 1)
acov_a = res.smoothed_state_autocov.transpose(2, 0, 1)
# E[a_t a_t'], t = 0, ..., T
Eaa = cov_a.copy() + np.matmul(a, a.transpose(0, 2, 1))
# E[a_t a_{t-1}'], t = 1, ..., T
Eaa1 = acov_a[:-1] + np.matmul(a[1:], a[:-1].transpose(0, 2, 1))
# Observation equation
has_missing = np.any(res.nmissing)
if mstep_method is None:
mstep_method = 'missing' if has_missing else 'nonmissing'
mstep_method = mstep_method.lower()
if mstep_method == 'nonmissing' and has_missing:
raise ValueError('Cannot use EM algorithm option'
' `mstep_method="nonmissing"` with missing data.')
if mstep_method == 'nonmissing':
func = self._em_maximization_obs_nonmissing
elif mstep_method == 'missing':
func = self._em_maximization_obs_missing
else:
raise ValueError('Invalid maximization step method: "%s".'
% mstep_method)
# TODO: compute H is pretty slow
Lambda, H = func(res, Eaa, a, compute_H=(not self.idiosyncratic_ar1))
# Factor VAR and covariance
factor_ar = []
factor_cov = []
for b in s.factor_blocks:
A = Eaa[:-1, b['factors_ar'], b['factors_ar']].sum(axis=0)
B = Eaa1[:, b['factors_L1'], b['factors_ar']].sum(axis=0)
C = Eaa[1:, b['factors_L1'], b['factors_L1']].sum(axis=0)
nobs = Eaa.shape[0] - 1
# want: x = B A^{-1}, so solve: x A = B or solve: A' x' = B'
try:
f_A = cho_solve(cho_factor(A), B.T).T
except LinAlgError:
# Fall back to general solver if there are problems with
# positive-definiteness
f_A = np.linalg.solve(A, B.T).T
f_Q = (C - f_A @ B.T) / nobs
factor_ar += f_A.ravel().tolist()
factor_cov += (
np.linalg.cholesky(f_Q)[np.tril_indices_from(f_Q)].tolist())
# Idiosyncratic AR(1) and variances
if self.idiosyncratic_ar1:
ix = s['idio_ar_L1']
Ad = Eaa[:-1, ix, ix].sum(axis=0).diagonal()
Bd = Eaa1[:, ix, ix].sum(axis=0).diagonal()
Cd = Eaa[1:, ix, ix].sum(axis=0).diagonal()
nobs = Eaa.shape[0] - 1
alpha = Bd / Ad
sigma2 = (Cd - alpha * Bd) / nobs
else:
ix = s['idio_ar_L1']
C = Eaa[:, ix, ix].sum(axis=0)
sigma2 = np.r_[H.diagonal()[self._o['M']],
C.diagonal() / Eaa.shape[0]]
# Save parameters
params1 = np.zeros_like(params0)
loadings = []
for i in range(self.k_endog):
iloc = self._s.endog_factor_iloc[i]
factor_ix = s['factors_L1'][iloc]
loadings += Lambda[i, factor_ix].tolist()
params1[self._p['loadings']] = loadings
params1[self._p['factor_ar']] = factor_ar
params1[self._p['factor_cov']] = factor_cov
if self.idiosyncratic_ar1:
params1[self._p['idiosyncratic_ar1']] = alpha
params1[self._p['idiosyncratic_var']] = sigma2
return params1
def _em_maximization_obs_nonmissing(self, res, Eaa, a, compute_H=False):
"""EM maximization step, observation equation without missing data."""
s = self._s
dtype = Eaa.dtype
# Observation equation (non-missing)
# Note: we only compute loadings for monthly variables because
# quarterly variables will always have missing entries, so we would
# never choose this method in that case
k = s.k_states_factors
Lambda = np.zeros((self.k_endog, k), dtype=dtype)
for i in range(self.k_endog):
y = self.endog[:, i:i + 1]
iloc = self._s.endog_factor_iloc[i]
factor_ix = s['factors_L1'][iloc]
ix = (np.s_[:],) + np.ix_(factor_ix, factor_ix)
A = Eaa[ix].sum(axis=0)
B = y.T @ a[:, factor_ix, 0]
if self.idiosyncratic_ar1:
ix1 = s.k_states_factors + i
ix2 = ix1 + 1
B -= Eaa[:, ix1:ix2, factor_ix].sum(axis=0)
# want: x = B A^{-1}, so solve: x A = B or solve: A' x' = B'
try:
Lambda[i, factor_ix] = cho_solve(cho_factor(A), B.T).T
except LinAlgError:
# Fall back to general solver if there are problems with
# positive-definiteness
Lambda[i, factor_ix] = np.linalg.solve(A, B.T).T
# Compute new obs cov
# Note: this is unnecessary if `idiosyncratic_ar1=True`.
# This is written in a slightly more general way than
# Banbura and Modugno (2014), equation (7); see instead equation (13)
# of Wu et al. (1996)
# "An algorithm for estimating parameters of state-space models"
if compute_H:
Z = self['design'].copy()
Z[:, :k] = Lambda
BL = self.endog.T @ a[..., 0] @ Z.T
C = self.endog.T @ self.endog
H = (C + -BL - BL.T + Z @ Eaa.sum(axis=0) @ Z.T) / self.nobs
else:
H = np.zeros((self.k_endog, self.k_endog), dtype=dtype) * np.nan
return Lambda, H
def _em_maximization_obs_missing(self, res, Eaa, a, compute_H=False):
"""EM maximization step, observation equation with missing data."""
s = self._s
dtype = Eaa.dtype
# Observation equation (missing)
k = s.k_states_factors
Lambda = np.zeros((self.k_endog, k), dtype=dtype)
W = (1 - res.missing.T)
mask = W.astype(bool)
# Compute design for monthly
# Note: the relevant A changes for each i
for i in range(self.k_endog_M):
iloc = self._s.endog_factor_iloc[i]
factor_ix = s['factors_L1'][iloc]
m = mask[:, i]
yt = self.endog[m, i:i + 1]
ix = np.ix_(m, factor_ix, factor_ix)
Ai = Eaa[ix].sum(axis=0)
Bi = yt.T @ a[np.ix_(m, factor_ix)][..., 0]
if self.idiosyncratic_ar1:
ix1 = s.k_states_factors + i
ix2 = ix1 + 1
Bi -= Eaa[m, ix1:ix2][..., factor_ix].sum(axis=0)
# want: x = B A^{-1}, so solve: x A = B or solve: A' x' = B'
try:
Lambda[i, factor_ix] = cho_solve(cho_factor(Ai), Bi.T).T
except LinAlgError:
# Fall back to general solver if there are problems with
# positive-definiteness
Lambda[i, factor_ix] = np.linalg.solve(Ai, Bi.T).T
# Compute unrestricted design for quarterly
# See Banbura at al. (2011), where this is described in Appendix C,
# between equations (13) and (14).
if self.k_endog_Q > 0:
# Note: the relevant A changes for each i
multipliers = np.array([1, 2, 3, 2, 1])[:, None]
for i in range(self.k_endog_M, self.k_endog):
iloc = self._s.endog_factor_iloc[i]
factor_ix = s['factors_L1_5_ix'][:, iloc].ravel().tolist()
R, _ = self.loading_constraints(i)
iQ = i - self.k_endog_M
m = mask[:, i]
yt = self.endog[m, i:i + 1]
ix = np.ix_(m, factor_ix, factor_ix)
Ai = Eaa[ix].sum(axis=0)
BiQ = yt.T @ a[np.ix_(m, factor_ix)][..., 0]
if self.idiosyncratic_ar1:
ix = (np.s_[:],) + np.ix_(s['idio_ar_Q_ix'][iQ], factor_ix)
Eepsf = Eaa[ix]
BiQ -= (multipliers * Eepsf[m].sum(axis=0)).sum(axis=0)
# Note that there was a typo in Banbura et al. (2011) for
# the formula applying the restrictions. In their notation,
# they show (C D C')^{-1} while it should be (C D^{-1} C')^{-1}
# Note: in reality, this is:
# unrestricted - Aii @ R.T @ RARi @ (R @ unrestricted - q)
# where the restrictions are defined as: R @ unrestricted = q
# However, here q = 0, so we can simplify.
try:
L_and_lower = cho_factor(Ai)
# x = BQ A^{-1}, or x A = BQ, so solve A' x' = (BQ)'
unrestricted = cho_solve(L_and_lower, BiQ.T).T[0]
AiiRT = cho_solve(L_and_lower, R.T)
L_and_lower = cho_factor(R @ AiiRT)
RAiiRTiR = cho_solve(L_and_lower, R)
restricted = unrestricted - AiiRT @ RAiiRTiR @ unrestricted
except LinAlgError:
# Fall back to slower method if there are problems with
# positive-definiteness
Aii = np.linalg.inv(Ai)
unrestricted = (BiQ @ Aii)[0]
RARi = np.linalg.inv(R @ Aii @ R.T)
restricted = (unrestricted -
Aii @ R.T @ RARi @ R @ unrestricted)
Lambda[i, factor_ix] = restricted
# Compute new obs cov
# Note: this is unnecessary if `idiosyncratic_ar1=True`.
# See Banbura and Modugno (2014), equation (12)
# This does not literally follow their formula, e.g. multiplying by the
# W_t selection matrices, because those formulas require loops that are
# relatively slow. The formulation here is vectorized.
if compute_H:
Z = self['design'].copy()
Z[:, :Lambda.shape[1]] = Lambda
y = np.nan_to_num(self.endog)
C = y.T @ y
W = W[..., None]
IW = 1 - W
WL = W * Z
WLT = WL.transpose(0, 2, 1)
BL = y[..., None] @ a.transpose(0, 2, 1) @ WLT
A = Eaa
BLT = BL.transpose(0, 2, 1)
IWT = IW.transpose(0, 2, 1)
H = (C + (-BL - BLT + WL @ A @ WLT +
IW * self['obs_cov'] * IWT).sum(axis=0)) / self.nobs
else:
H = np.zeros((self.k_endog, self.k_endog), dtype=dtype) * np.nan
return Lambda, H
[docs]
def smooth(self, params, transformed=True, includes_fixed=False,
complex_step=False, cov_type='none', cov_kwds=None,
return_ssm=False, results_class=None,
results_wrapper_class=None, **kwargs):
"""
Kalman smoothing.
Parameters
----------
params : array_like
Array of parameters at which to evaluate the loglikelihood
function.
transformed : bool, optional
Whether or not `params` is already transformed. Default is True.
return_ssm : bool,optional
Whether or not to return only the state space output or a full
results object. Default is to return a full results object.
cov_type : str, optional
See `MLEResults.fit` for a description of covariance matrix types
for results object. Default is None.
cov_kwds : dict or None, optional
See `MLEResults.get_robustcov_results` for a description required
keywords for alternative covariance estimators
**kwargs
Additional keyword arguments to pass to the Kalman filter. See
`KalmanFilter.filter` for more details.
"""
return super().smooth(
params, transformed=transformed, includes_fixed=includes_fixed,
complex_step=complex_step, cov_type=cov_type, cov_kwds=cov_kwds,
return_ssm=return_ssm, results_class=results_class,
results_wrapper_class=results_wrapper_class, **kwargs)
[docs]
def filter(self, params, transformed=True, includes_fixed=False,
complex_step=False, cov_type='none', cov_kwds=None,
return_ssm=False, results_class=None,
results_wrapper_class=None, low_memory=False, **kwargs):
"""
Kalman filtering.
Parameters
----------
params : array_like
Array of parameters at which to evaluate the loglikelihood
function.
transformed : bool, optional
Whether or not `params` is already transformed. Default is True.
return_ssm : bool,optional
Whether or not to return only the state space output or a full
results object. Default is to return a full results object.
cov_type : str, optional
See `MLEResults.fit` for a description of covariance matrix types
for results object. Default is 'none'.
cov_kwds : dict or None, optional
See `MLEResults.get_robustcov_results` for a description required
keywords for alternative covariance estimators
low_memory : bool, optional
If set to True, techniques are applied to substantially reduce
memory usage. If used, some features of the results object will
not be available (including in-sample prediction), although
out-of-sample forecasting is possible. Default is False.
**kwargs
Additional keyword arguments to pass to the Kalman filter. See
`KalmanFilter.filter` for more details.
"""
return super().filter(
params, transformed=transformed, includes_fixed=includes_fixed,
complex_step=complex_step, cov_type=cov_type, cov_kwds=cov_kwds,
return_ssm=return_ssm, results_class=results_class,
results_wrapper_class=results_wrapper_class, **kwargs)
[docs]
def simulate(self, params, nsimulations, measurement_shocks=None,
state_shocks=None, initial_state=None, anchor=None,
repetitions=None, exog=None, extend_model=None,
extend_kwargs=None, transformed=True, includes_fixed=False,
original_scale=True, **kwargs):
r"""
Simulate a new time series following the state space model.
Parameters
----------
params : array_like
Array of parameters to use in constructing the state space
representation to use when simulating.
nsimulations : int
The number of observations to simulate. If the model is
time-invariant this can be any number. If the model is
time-varying, then this number must be less than or equal to the
number of observations.
measurement_shocks : array_like, optional
If specified, these are the shocks to the measurement equation,
:math:`\varepsilon_t`. If unspecified, these are automatically
generated using a pseudo-random number generator. If specified,
must be shaped `nsimulations` x `k_endog`, where `k_endog` is the
same as in the state space model.
state_shocks : array_like, optional
If specified, these are the shocks to the state equation,
:math:`\eta_t`. If unspecified, these are automatically
generated using a pseudo-random number generator. If specified,
must be shaped `nsimulations` x `k_posdef` where `k_posdef` is the
same as in the state space model.
initial_state : array_like, optional
If specified, this is the initial state vector to use in
simulation, which should be shaped (`k_states` x 1), where
`k_states` is the same as in the state space model. If unspecified,
but the model has been initialized, then that initialization is
used. This must be specified if `anchor` is anything other than
"start" or 0 (or else you can use the `simulate` method on a
results object rather than on the model object).
anchor : int, str, or datetime, optional
First period for simulation. The simulation will be conditional on
all existing datapoints prior to the `anchor`. Type depends on the
index of the given `endog` in the model. Two special cases are the
strings 'start' and 'end'. `start` refers to beginning the
simulation at the first period of the sample, and `end` refers to
beginning the simulation at the first period after the sample.
Integer values can run from 0 to `nobs`, or can be negative to
apply negative indexing. Finally, if a date/time index was provided
to the model, then this argument can be a date string to parse or a
datetime type. Default is 'start'.
repetitions : int, optional
Number of simulated paths to generate. Default is 1 simulated path.
exog : array_like, optional
New observations of exogenous regressors, if applicable.
transformed : bool, optional
Whether or not `params` is already transformed. Default is
True.
includes_fixed : bool, optional
If parameters were previously fixed with the `fix_params` method,
this argument describes whether or not `params` also includes
the fixed parameters, in addition to the free parameters. Default
is False.
original_scale : bool, optional
If the model specification standardized the data, whether or not
to return simulations in the original scale of the data (i.e.
before it was standardized by the model). Default is True.
Returns
-------
simulated_obs : ndarray
An array of simulated observations. If `repetitions=None`, then it
will be shaped (nsimulations x k_endog) or (nsimulations,) if
`k_endog=1`. Otherwise it will be shaped
(nsimulations x k_endog x repetitions). If the model was given
Pandas input then the output will be a Pandas object. If
`k_endog > 1` and `repetitions` is not None, then the output will
be a Pandas DataFrame that has a MultiIndex for the columns, with
the first level containing the names of the `endog` variables and
the second level containing the repetition number.
"""
# Get usual simulations (in the possibly-standardized scale)
sim = super().simulate(
params, nsimulations, measurement_shocks=measurement_shocks,
state_shocks=state_shocks, initial_state=initial_state,
anchor=anchor, repetitions=repetitions, exog=exog,
extend_model=extend_model, extend_kwargs=extend_kwargs,
transformed=transformed, includes_fixed=includes_fixed, **kwargs)
# If applicable, convert predictions back to original space
if self.standardize and original_scale:
use_pandas = isinstance(self.data, PandasData)
shape = sim.shape
if use_pandas:
# pd.Series (k_endog=1, replications=None)
if len(shape) == 1:
std = self._endog_std.iloc[0]
mean = self._endog_mean.iloc[0]
sim = sim * std + mean
# pd.DataFrame (k_endog > 1, replications=None)
# [or]
# pd.DataFrame with MultiIndex (replications > 0)
elif len(shape) == 2:
sim = (sim.multiply(self._endog_std, axis=1, level=0)
.add(self._endog_mean, axis=1, level=0))
else:
# 1-dim array (k_endog=1, replications=None)
if len(shape) == 1:
sim = sim * self._endog_std + self._endog_mean
# 2-dim array (k_endog > 1, replications=None)
elif len(shape) == 2:
sim = sim * self._endog_std + self._endog_mean
# 3-dim array with MultiIndex (replications > 0)
else:
# Get arrays into the form that can be used for
# broadcasting
std = np.atleast_2d(self._endog_std)[..., None]
mean = np.atleast_2d(self._endog_mean)[..., None]
sim = sim * std + mean
return sim
[docs]
def impulse_responses(self, params, steps=1, impulse=0,
orthogonalized=False, cumulative=False, anchor=None,
exog=None, extend_model=None, extend_kwargs=None,
transformed=True, includes_fixed=False,
original_scale=True, **kwargs):
"""
Impulse response function.
Parameters
----------
params : array_like
Array of model parameters.
steps : int, optional
The number of steps for which impulse responses are calculated.
Default is 1. Note that for time-invariant models, the initial
impulse is not counted as a step, so if `steps=1`, the output will
have 2 entries.
impulse : int or array_like
If an integer, the state innovation to pulse; must be between 0
and `k_posdef-1`. Alternatively, a custom impulse vector may be
provided; must be shaped `k_posdef x 1`.
orthogonalized : bool, optional
Whether or not to perform impulse using orthogonalized innovations.
Note that this will also affect custum `impulse` vectors. Default
is False.
cumulative : bool, optional
Whether or not to return cumulative impulse responses. Default is
False.
anchor : int, str, or datetime, optional
Time point within the sample for the state innovation impulse. Type
depends on the index of the given `endog` in the model. Two special
cases are the strings 'start' and 'end', which refer to setting the
impulse at the first and last points of the sample, respectively.
Integer values can run from 0 to `nobs - 1`, or can be negative to
apply negative indexing. Finally, if a date/time index was provided
to the model, then this argument can be a date string to parse or a
datetime type. Default is 'start'.
exog : array_like, optional
New observations of exogenous regressors for our-of-sample periods,
if applicable.
transformed : bool, optional
Whether or not `params` is already transformed. Default is
True.
includes_fixed : bool, optional
If parameters were previously fixed with the `fix_params` method,
this argument describes whether or not `params` also includes
the fixed parameters, in addition to the free parameters. Default
is False.
original_scale : bool, optional
If the model specification standardized the data, whether or not
to return impulse responses in the original scale of the data (i.e.
before it was standardized by the model). Default is True.
**kwargs
If the model has time-varying design or transition matrices and the
combination of `anchor` and `steps` implies creating impulse
responses for the out-of-sample period, then these matrices must
have updated values provided for the out-of-sample steps. For
example, if `design` is a time-varying component, `nobs` is 10,
`anchor=1`, and `steps` is 15, a (`k_endog` x `k_states` x 7)
matrix must be provided with the new design matrix values.
Returns
-------
impulse_responses : ndarray
Responses for each endogenous variable due to the impulse
given by the `impulse` argument. For a time-invariant model, the
impulse responses are given for `steps + 1` elements (this gives
the "initial impulse" followed by `steps` responses for the
important cases of VAR and SARIMAX models), while for time-varying
models the impulse responses are only given for `steps` elements
(to avoid having to unexpectedly provide updated time-varying
matrices).
"""
# Get usual simulations (in the possibly-standardized scale)
irfs = super().impulse_responses(
params, steps=steps, impulse=impulse,
orthogonalized=orthogonalized, cumulative=cumulative,
anchor=anchor, exog=exog, extend_model=extend_model,
extend_kwargs=extend_kwargs, transformed=transformed,
includes_fixed=includes_fixed, **kwargs)
# If applicable, convert predictions back to original space
if self.standardize and original_scale:
use_pandas = isinstance(self.data, PandasData)
shape = irfs.shape
if use_pandas:
# pd.Series (k_endog=1, replications=None)
if len(shape) == 1:
irfs = irfs * self._endog_std.iloc[0]
# pd.DataFrame (k_endog > 1)
# [or]
# pd.DataFrame with MultiIndex (replications > 0)
elif len(shape) == 2:
irfs = irfs.multiply(self._endog_std, axis=1, level=0)
else:
# 1-dim array (k_endog=1)
if len(shape) == 1:
irfs = irfs * self._endog_std
# 2-dim array (k_endog > 1)
elif len(shape) == 2:
irfs = irfs * self._endog_std
return irfs
[docs]
class DynamicFactorMQResults(mlemodel.MLEResults):
"""
Results from fitting a dynamic factor model
"""
def __init__(self, model, params, filter_results, cov_type=None, **kwargs):
super().__init__(
model, params, filter_results, cov_type, **kwargs)
@property
def factors(self):
"""
Estimates of unobserved factors.
Returns
-------
out : Bunch
Has the following attributes shown in Notes.
Notes
-----
The output is a bunch of the following format:
- `filtered`: a time series array with the filtered estimate of
the component
- `filtered_cov`: a time series array with the filtered estimate of
the variance/covariance of the component
- `smoothed`: a time series array with the smoothed estimate of
the component
- `smoothed_cov`: a time series array with the smoothed estimate of
the variance/covariance of the component
- `offset`: an integer giving the offset in the state vector where
this component begins
"""
out = None
if self.model.k_factors > 0:
iloc = self.model._s.factors_L1
ix = np.array(self.model.state_names)[iloc].tolist()
out = Bunch(
filtered=self.states.filtered.loc[:, ix],
filtered_cov=self.states.filtered_cov.loc[np.s_[ix, :], ix],
smoothed=None, smoothed_cov=None)
if self.smoothed_state is not None:
out.smoothed = self.states.smoothed.loc[:, ix]
if self.smoothed_state_cov is not None:
out.smoothed_cov = (
self.states.smoothed_cov.loc[np.s_[ix, :], ix])
return out
[docs]
def get_coefficients_of_determination(self, method='individual',
which=None):
"""
Get coefficients of determination (R-squared) for variables / factors.
Parameters
----------
method : {'individual', 'joint', 'cumulative'}, optional
The type of R-squared values to generate. "individual" plots
the R-squared of each variable on each factor; "joint" plots the
R-squared of each variable on each factor that it loads on;
"cumulative" plots the successive R-squared values as each
additional factor is added to the regression, for each variable.
Default is 'individual'.
which: {None, 'filtered', 'smoothed'}, optional
Whether to compute R-squared values based on filtered or smoothed
estimates of the factors. Default is 'smoothed' if smoothed results
are available and 'filtered' otherwise.
Returns
-------
rsquared : pd.DataFrame or pd.Series
The R-squared values from regressions of observed variables on
one or more of the factors. If method='individual' or
method='cumulative', this will be a Pandas DataFrame with observed
variables as the index and factors as the columns . If
method='joint', will be a Pandas Series with observed variables as
the index.
See Also
--------
plot_coefficients_of_determination
coefficients_of_determination
"""
from statsmodels.tools import add_constant
method = string_like(method, 'method', options=['individual', 'joint',
'cumulative'])
if which is None:
which = 'filtered' if self.smoothed_state is None else 'smoothed'
k_endog = self.model.k_endog
k_factors = self.model.k_factors
ef_map = self.model._s.endog_factor_map
endog_names = self.model.endog_names
factor_names = self.model.factor_names
if method == 'individual':
coefficients = np.zeros((k_endog, k_factors))
for i in range(k_factors):
exog = add_constant(self.factors[which].iloc[:, i])
for j in range(k_endog):
if ef_map.iloc[j, i]:
endog = self.filter_results.endog[j]
coefficients[j, i] = (
OLS(endog, exog, missing='drop').fit().rsquared)
else:
coefficients[j, i] = np.nan
coefficients = pd.DataFrame(coefficients, index=endog_names,
columns=factor_names)
elif method == 'joint':
coefficients = np.zeros((k_endog,))
exog = add_constant(self.factors[which])
for j in range(k_endog):
endog = self.filter_results.endog[j]
ix = np.r_[True, ef_map.iloc[j]].tolist()
X = exog.loc[:, ix]
coefficients[j] = (
OLS(endog, X, missing='drop').fit().rsquared)
coefficients = pd.Series(coefficients, index=endog_names)
elif method == 'cumulative':
coefficients = np.zeros((k_endog, k_factors))
exog = add_constant(self.factors[which])
for j in range(k_endog):
endog = self.filter_results.endog[j]
for i in range(k_factors):
if self.model._s.endog_factor_map.iloc[j, i]:
ix = np.r_[True, ef_map.iloc[j, :i + 1],
[False] * (k_factors - i - 1)]
X = exog.loc[:, ix.astype(bool).tolist()]
coefficients[j, i] = (
OLS(endog, X, missing='drop').fit().rsquared)
else:
coefficients[j, i] = np.nan
coefficients = pd.DataFrame(coefficients, index=endog_names,
columns=factor_names)
return coefficients
@cache_readonly
def coefficients_of_determination(self):
"""
Individual coefficients of determination (:math:`R^2`).
Coefficients of determination (:math:`R^2`) from regressions of
endogenous variables on individual estimated factors.
Returns
-------
coefficients_of_determination : ndarray
A `k_endog` x `k_factors` array, where
`coefficients_of_determination[i, j]` represents the :math:`R^2`
value from a regression of factor `j` and a constant on endogenous
variable `i`.
Notes
-----
Although it can be difficult to interpret the estimated factor loadings
and factors, it is often helpful to use the coefficients of
determination from univariate regressions to assess the importance of
each factor in explaining the variation in each endogenous variable.
In models with many variables and factors, this can sometimes lend
interpretation to the factors (for example sometimes one factor will
load primarily on real variables and another on nominal variables).
See Also
--------
get_coefficients_of_determination
plot_coefficients_of_determination
"""
return self.get_coefficients_of_determination(method='individual')
[docs]
def plot_coefficients_of_determination(self, method='individual',
which=None, endog_labels=None,
fig=None, figsize=None):
"""
Plot coefficients of determination (R-squared) for variables / factors.
Parameters
----------
method : {'individual', 'joint', 'cumulative'}, optional
The type of R-squared values to generate. "individual" plots
the R-squared of each variable on each factor; "joint" plots the
R-squared of each variable on each factor that it loads on;
"cumulative" plots the successive R-squared values as each
additional factor is added to the regression, for each variable.
Default is 'individual'.
which: {None, 'filtered', 'smoothed'}, optional
Whether to compute R-squared values based on filtered or smoothed
estimates of the factors. Default is 'smoothed' if smoothed results
are available and 'filtered' otherwise.
endog_labels : bool, optional
Whether or not to label the endogenous variables along the x-axis
of the plots. Default is to include labels if there are 5 or fewer
endogenous variables.
fig : Figure, optional
If given, subplots are created in this figure instead of in a new
figure. Note that the grid will be created in the provided
figure using `fig.add_subplot()`.
figsize : tuple, optional
If a figure is created, this argument allows specifying a size.
The tuple is (width, height).
Notes
-----
The endogenous variables are arranged along the x-axis according to
their position in the model's `endog` array.
See Also
--------
get_coefficients_of_determination
"""
from statsmodels.graphics.utils import _import_mpl, create_mpl_fig
_import_mpl()
fig = create_mpl_fig(fig, figsize)
method = string_like(method, 'method', options=['individual', 'joint',
'cumulative'])
# Should we label endogenous variables?
if endog_labels is None:
endog_labels = self.model.k_endog <= 5
# Plot the coefficients of determination
rsquared = self.get_coefficients_of_determination(method=method,
which=which)
if method in ['individual', 'cumulative']:
plot_idx = 1
for factor_name, coeffs in rsquared.T.iterrows():
# Create the new axis
ax = fig.add_subplot(self.model.k_factors, 1, plot_idx)
ax.set_ylim((0, 1))
ax.set(title=f'{factor_name}', ylabel=r'$R^2$')
coeffs.plot(ax=ax, kind='bar')
if plot_idx < len(rsquared.columns) or not endog_labels:
ax.xaxis.set_ticklabels([])
plot_idx += 1
elif method == 'joint':
ax = fig.add_subplot(1, 1, 1)
ax.set_ylim((0, 1))
ax.set(title=r'$R^2$ - regression on all loaded factors',
ylabel=r'$R^2$')
rsquared.plot(ax=ax, kind='bar')
if not endog_labels:
ax.xaxis.set_ticklabels([])
return fig
[docs]
def get_prediction(self, start=None, end=None, dynamic=False,
information_set='predicted', signal_only=False,
original_scale=True, index=None, exog=None,
extend_model=None, extend_kwargs=None, **kwargs):
r"""
In-sample prediction and out-of-sample forecasting.
Parameters
----------
start : int, str, or datetime, optional
Zero-indexed observation number at which to start forecasting,
i.e., the first forecast is start. Can also be a date string to
parse or a datetime type. Default is the the zeroth observation.
end : int, str, or datetime, optional
Zero-indexed observation number at which to end forecasting, i.e.,
the last forecast is end. Can also be a date string to
parse or a datetime type. However, if the dates index does not
have a fixed frequency, end must be an integer index if you
want out of sample prediction. Default is the last observation in
the sample.
dynamic : bool, int, str, or datetime, optional
Integer offset relative to `start` at which to begin dynamic
prediction. Can also be an absolute date string to parse or a
datetime type (these are not interpreted as offsets).
Prior to this observation, true endogenous values will be used for
prediction; starting with this observation and continuing through
the end of prediction, forecasted endogenous values will be used
instead.
information_set : str, optional
The information set to condition each prediction on. Default is
"predicted", which computes predictions of period t values
conditional on observed data through period t-1; these are
one-step-ahead predictions, and correspond with the typical
`fittedvalues` results attribute. Alternatives are "filtered",
which computes predictions of period t values conditional on
observed data through period t, and "smoothed", which computes
predictions of period t values conditional on the entire dataset
(including also future observations t+1, t+2, ...).
signal_only : bool, optional
Whether to compute forecasts of only the "signal" component of
the observation equation. Default is False. For example, the
observation equation of a time-invariant model is
:math:`y_t = d + Z \alpha_t + \varepsilon_t`, and the "signal"
component is then :math:`Z \alpha_t`. If this argument is set to
True, then forecasts of the "signal" :math:`Z \alpha_t` will be
returned. Otherwise, the default is for forecasts of :math:`y_t`
to be returned.
original_scale : bool, optional
If the model specification standardized the data, whether or not
to return predictions in the original scale of the data (i.e.
before it was standardized by the model). Default is True.
**kwargs
Additional arguments may required for forecasting beyond the end
of the sample. See `FilterResults.predict` for more details.
Returns
-------
forecast : ndarray
Array of out of in-sample predictions and / or out-of-sample
forecasts. An (npredict x k_endog) array.
"""
# Get usual predictions (in the possibly-standardized scale)
res = super().get_prediction(start=start, end=end, dynamic=dynamic,
information_set=information_set,
signal_only=signal_only,
index=index, exog=exog,
extend_model=extend_model,
extend_kwargs=extend_kwargs, **kwargs)
# If applicable, convert predictions back to original space
if self.model.standardize and original_scale:
prediction_results = res.prediction_results
k_endog, _ = prediction_results.endog.shape
mean = np.array(self.model._endog_mean)
std = np.array(self.model._endog_std)
if self.model.k_endog > 1:
mean = mean[None, :]
std = std[None, :]
res._results._predicted_mean = (
res._results._predicted_mean * std + mean)
if k_endog == 1:
res._results._var_pred_mean *= std**2
else:
res._results._var_pred_mean = (
std * res._results._var_pred_mean * std.T)
return res
[docs]
def news(self, comparison, impact_date=None, impacted_variable=None,
start=None, end=None, periods=None, exog=None,
comparison_type=None, revisions_details_start=False,
state_index=None, return_raw=False, tolerance=1e-10,
endog_quarterly=None, original_scale=True, **kwargs):
"""
Compute impacts from updated data (news and revisions).
Parameters
----------
comparison : array_like or MLEResults
An updated dataset with updated and/or revised data from which the
news can be computed, or an updated or previous results object
to use in computing the news.
impact_date : int, str, or datetime, optional
A single specific period of impacts from news and revisions to
compute. Can also be a date string to parse or a datetime type.
This argument cannot be used in combination with `start`, `end`, or
`periods`. Default is the first out-of-sample observation.
impacted_variable : str, list, array, or slice, optional
Observation variable label or slice of labels specifying that only
specific impacted variables should be shown in the News output. The
impacted variable(s) describe the variables that were *affected* by
the news. If you do not know the labels for the variables, check
the `endog_names` attribute of the model instance.
start : int, str, or datetime, optional
The first period of impacts from news and revisions to compute.
Can also be a date string to parse or a datetime type. Default is
the first out-of-sample observation.
end : int, str, or datetime, optional
The last period of impacts from news and revisions to compute.
Can also be a date string to parse or a datetime type. Default is
the first out-of-sample observation.
periods : int, optional
The number of periods of impacts from news and revisions to
compute.
exog : array_like, optional
Array of exogenous regressors for the out-of-sample period, if
applicable.
comparison_type : {None, 'previous', 'updated'}
This denotes whether the `comparison` argument represents a
*previous* results object or dataset or an *updated* results object
or dataset. If not specified, then an attempt is made to determine
the comparison type.
state_index : array_like or "common", optional
An optional index specifying a subset of states to use when
constructing the impacts of revisions and news. For example, if
`state_index=[0, 1]` is passed, then only the impacts to the
observed variables arising from the impacts to the first two
states will be returned. If the string "common" is passed and the
model includes idiosyncratic AR(1) components, news will only be
computed based on the common states. Default is to use all states.
return_raw : bool, optional
Whether or not to return only the specific output or a full
results object. Default is to return a full results object.
tolerance : float, optional
The numerical threshold for determining zero impact. Default is
that any impact less than 1e-10 is assumed to be zero.
endog_quarterly : array_like, optional
New observations of quarterly variables, if `comparison` was
provided as an updated monthly dataset. If this argument is
provided, it must be a Pandas Series or DataFrame with a
DatetimeIndex or PeriodIndex at the quarterly frequency.
References
----------
.. [1] Bańbura, Marta, and Michele Modugno.
"Maximum likelihood estimation of factor models on datasets with
arbitrary pattern of missing data."
Journal of Applied Econometrics 29, no. 1 (2014): 133-160.
.. [2] Bańbura, Marta, Domenico Giannone, and Lucrezia Reichlin.
"Nowcasting."
The Oxford Handbook of Economic Forecasting. July 8, 2011.
.. [3] Bańbura, Marta, Domenico Giannone, Michele Modugno, and Lucrezia
Reichlin.
"Now-casting and the real-time data flow."
In Handbook of economic forecasting, vol. 2, pp. 195-237.
Elsevier, 2013.
"""
if state_index == 'common':
state_index = (
np.arange(self.model.k_states - self.model.k_endog))
news_results = super().news(
comparison, impact_date=impact_date,
impacted_variable=impacted_variable, start=start, end=end,
periods=periods, exog=exog, comparison_type=comparison_type,
revisions_details_start=revisions_details_start,
state_index=state_index, return_raw=return_raw,
tolerance=tolerance, endog_quarterly=endog_quarterly, **kwargs)
# If we have standardized the data, we may want to report the news in
# the original scale. If so, we need to modify the data to "undo" the
# standardization.
if not return_raw and self.model.standardize and original_scale:
endog_mean = self.model._endog_mean
endog_std = self.model._endog_std
# Don't need to add in the mean for the impacts, since they are
# the difference of two forecasts
news_results.total_impacts = (
news_results.total_impacts * endog_std)
news_results.update_impacts = (
news_results.update_impacts * endog_std)
if news_results.revision_impacts is not None:
news_results.revision_impacts = (
news_results.revision_impacts * endog_std)
if news_results.revision_detailed_impacts is not None:
news_results.revision_detailed_impacts = (
news_results.revision_detailed_impacts * endog_std)
if news_results.revision_grouped_impacts is not None:
news_results.revision_grouped_impacts = (
news_results.revision_grouped_impacts * endog_std)
# Update forecasts
for name in ['prev_impacted_forecasts', 'news', 'revisions',
'update_realized', 'update_forecasts',
'revised', 'revised_prev', 'post_impacted_forecasts',
'revisions_all', 'revised_all', 'revised_prev_all']:
dta = getattr(news_results, name)
# for pd.Series, dta.multiply(...) and (sometimes) dta.add(...)
# remove the name attribute; save it now so that we can add it
# back in
orig_name = None
if hasattr(dta, 'name'):
orig_name = dta.name
dta = dta.multiply(endog_std, level=1)
if name not in ['news', 'revisions']:
dta = dta.add(endog_mean, level=1)
# add back in the name attribute if it was removed
if orig_name is not None:
dta.name = orig_name
setattr(news_results, name, dta)
# For the weights: rows correspond to update (date, variable) and
# columns correspond to the impacted variable.
# 1. Because we have modified the updates (realized, forecasts, and
# forecast errors) to be in the scale of the original updated
# variable, we need to essentially reverse that change for each
# row of the weights by dividing by the standard deviation of
# that row's updated variable
# 2. Because we want the impacts to be in the scale of the original
# impacted variable, we need to multiply each column by the
# standard deviation of that column's impacted variable
news_results.weights = (
news_results.weights.divide(endog_std, axis=0, level=1)
.multiply(endog_std, axis=1, level=1))
news_results.revision_weights = (
news_results.revision_weights
.divide(endog_std, axis=0, level=1)
.multiply(endog_std, axis=1, level=1))
return news_results
[docs]
def get_smoothed_decomposition(self, decomposition_of='smoothed_state',
state_index=None, original_scale=True):
r"""
Decompose smoothed output into contributions from observations
Parameters
----------
decomposition_of : {"smoothed_state", "smoothed_signal"}
The object to perform a decomposition of. If it is set to
"smoothed_state", then the elements of the smoothed state vector
are decomposed into the contributions of each observation. If it
is set to "smoothed_signal", then the predictions of the
observation vector based on the smoothed state vector are
decomposed. Default is "smoothed_state".
state_index : array_like, optional
An optional index specifying a subset of states to use when
constructing the decomposition of the "smoothed_signal". For
example, if `state_index=[0, 1]` is passed, then only the
contributions of observed variables to the smoothed signal arising
from the first two states will be returned. Note that if not all
states are used, the contributions will not sum to the smoothed
signal. Default is to use all states.
original_scale : bool, optional
If the model specification standardized the data, whether or not
to return simulations in the original scale of the data (i.e.
before it was standardized by the model). Default is True.
Returns
-------
data_contributions : pd.DataFrame
Contributions of observations to the decomposed object. If the
smoothed state is being decomposed, then `data_contributions` is
shaped `(k_states x nobs, k_endog x nobs)` with a `pd.MultiIndex`
index corresponding to `state_to x date_to` and `pd.MultiIndex`
columns corresponding to `variable_from x date_from`. If the
smoothed signal is being decomposed, then `data_contributions` is
shaped `(k_endog x nobs, k_endog x nobs)` with `pd.MultiIndex`-es
corresponding to `variable_to x date_to` and
`variable_from x date_from`.
obs_intercept_contributions : pd.DataFrame
Contributions of the observation intercept to the decomposed
object. If the smoothed state is being decomposed, then
`obs_intercept_contributions` is
shaped `(k_states x nobs, k_endog x nobs)` with a `pd.MultiIndex`
index corresponding to `state_to x date_to` and `pd.MultiIndex`
columns corresponding to `obs_intercept_from x date_from`. If the
smoothed signal is being decomposed, then
`obs_intercept_contributions` is shaped
`(k_endog x nobs, k_endog x nobs)` with `pd.MultiIndex`-es
corresponding to `variable_to x date_to` and
`obs_intercept_from x date_from`.
state_intercept_contributions : pd.DataFrame
Contributions of the state intercept to the decomposed
object. If the smoothed state is being decomposed, then
`state_intercept_contributions` is
shaped `(k_states x nobs, k_states x nobs)` with a `pd.MultiIndex`
index corresponding to `state_to x date_to` and `pd.MultiIndex`
columns corresponding to `state_intercept_from x date_from`. If the
smoothed signal is being decomposed, then
`state_intercept_contributions` is shaped
`(k_endog x nobs, k_states x nobs)` with `pd.MultiIndex`-es
corresponding to `variable_to x date_to` and
`state_intercept_from x date_from`.
prior_contributions : pd.DataFrame
Contributions of the prior to the decomposed object. If the
smoothed state is being decomposed, then `prior_contributions` is
shaped `(nobs x k_states, k_states)`, with a `pd.MultiIndex`
index corresponding to `state_to x date_to` and columns
corresponding to elements of the prior mean (aka "initial state").
If the smoothed signal is being decomposed, then
`prior_contributions` is shaped `(nobs x k_endog, k_states)`,
with a `pd.MultiIndex` index corresponding to
`variable_to x date_to` and columns corresponding to elements of
the prior mean.
Notes
-----
Denote the smoothed state at time :math:`t` by :math:`\alpha_t`. Then
the smoothed signal is :math:`Z_t \alpha_t`, where :math:`Z_t` is the
design matrix operative at time :math:`t`.
"""
# De-meaning the data is like putting the mean into the observation
# intercept. To compute the decomposition correctly in the original
# scale, we need to account for this, so we fill in the observation
# intercept temporarily
if self.model.standardize and original_scale:
cache_obs_intercept = self.model['obs_intercept']
self.model['obs_intercept'] = self.model._endog_mean
# Compute the contributions
(data_contributions, obs_intercept_contributions,
state_intercept_contributions, prior_contributions) = (
super().get_smoothed_decomposition(
decomposition_of=decomposition_of, state_index=state_index))
# Replace the original observation intercept
if self.model.standardize and original_scale:
self.model['obs_intercept'] = cache_obs_intercept
# Reverse the effect of dividing by the standard deviation
if (decomposition_of == 'smoothed_signal'
and self.model.standardize and original_scale):
endog_std = self.model._endog_std
data_contributions = (
data_contributions.multiply(endog_std, axis=0, level=0))
obs_intercept_contributions = (
obs_intercept_contributions.multiply(
endog_std, axis=0, level=0))
state_intercept_contributions = (
state_intercept_contributions.multiply(
endog_std, axis=0, level=0))
prior_contributions = (
prior_contributions.multiply(endog_std, axis=0, level=0))
return (data_contributions, obs_intercept_contributions,
state_intercept_contributions, prior_contributions)
[docs]
def append(self, endog, endog_quarterly=None, refit=False, fit_kwargs=None,
copy_initialization=True, retain_standardization=True,
**kwargs):
"""
Recreate the results object with new data appended to original data.
Creates a new result object applied to a dataset that is created by
appending new data to the end of the model's original data. The new
results can then be used for analysis or forecasting.
Parameters
----------
endog : array_like
New observations from the modeled time-series process.
endog_quarterly : array_like, optional
New observations of quarterly variables. If provided, must be a
Pandas Series or DataFrame with a DatetimeIndex or PeriodIndex at
the quarterly frequency.
refit : bool, optional
Whether to re-fit the parameters, based on the combined dataset.
Default is False (so parameters from the current results object
are used to create the new results object).
fit_kwargs : dict, optional
Keyword arguments to pass to `fit` (if `refit=True`) or `filter` /
`smooth`.
copy_initialization : bool, optional
Whether or not to copy the initialization from the current results
set to the new model. Default is True.
retain_standardization : bool, optional
Whether or not to use the mean and standard deviations that were
used to standardize the data in the current model in the new model.
Default is True.
**kwargs
Keyword arguments may be used to modify model specification
arguments when created the new model object.
Returns
-------
results
Updated Results object, that includes results from both the
original dataset and the new dataset.
Notes
-----
The `endog` and `exog` arguments to this method must be formatted in
the same way (e.g. Pandas Series versus Numpy array) as were the
`endog` and `exog` arrays passed to the original model.
The `endog` (and, if applicable, `endog_quarterly`) arguments to this
method should consist of new observations that occurred directly after
the last element of `endog`. For any other kind of dataset, see the
`apply` method.
This method will apply filtering to all of the original data as well
as to the new data. To apply filtering only to the new data (which
can be much faster if the original dataset is large), see the `extend`
method.
See Also
--------
extend
apply
"""
# Construct the combined dataset, if necessary
endog, k_endog_monthly = DynamicFactorMQ.construct_endog(
endog, endog_quarterly)
# Check for compatible dimensions
k_endog = endog.shape[1] if len(endog.shape) == 2 else 1
if (k_endog_monthly != self.model.k_endog_M or
k_endog != self.model.k_endog):
raise ValueError('Cannot append data of a different dimension to'
' a model.')
kwargs['k_endog_monthly'] = k_endog_monthly
return super().append(
endog, refit=refit, fit_kwargs=fit_kwargs,
copy_initialization=copy_initialization,
retain_standardization=retain_standardization, **kwargs)
[docs]
def extend(self, endog, endog_quarterly=None, fit_kwargs=None,
retain_standardization=True, **kwargs):
"""
Recreate the results object for new data that extends original data.
Creates a new result object applied to a new dataset that is assumed to
follow directly from the end of the model's original data. The new
results can then be used for analysis or forecasting.
Parameters
----------
endog : array_like
New observations from the modeled time-series process.
endog_quarterly : array_like, optional
New observations of quarterly variables. If provided, must be a
Pandas Series or DataFrame with a DatetimeIndex or PeriodIndex at
the quarterly frequency.
fit_kwargs : dict, optional
Keyword arguments to pass to `filter` or `smooth`.
retain_standardization : bool, optional
Whether or not to use the mean and standard deviations that were
used to standardize the data in the current model in the new model.
Default is True.
**kwargs
Keyword arguments may be used to modify model specification
arguments when created the new model object.
Returns
-------
results
Updated Results object, that includes results only for the new
dataset.
See Also
--------
append
apply
Notes
-----
The `endog` argument to this method should consist of new observations
that occurred directly after the last element of the model's original
`endog` array. For any other kind of dataset, see the `apply` method.
This method will apply filtering only to the new data provided by the
`endog` argument, which can be much faster than re-filtering the entire
dataset. However, the returned results object will only have results
for the new data. To retrieve results for both the new data and the
original data, see the `append` method.
"""
# Construct the combined dataset, if necessary
endog, k_endog_monthly = DynamicFactorMQ.construct_endog(
endog, endog_quarterly)
# Check for compatible dimensions
k_endog = endog.shape[1] if len(endog.shape) == 2 else 1
if (k_endog_monthly != self.model.k_endog_M or
k_endog != self.model.k_endog):
raise ValueError('Cannot append data of a different dimension to'
' a model.')
kwargs['k_endog_monthly'] = k_endog_monthly
return super().extend(
endog, fit_kwargs=fit_kwargs,
retain_standardization=retain_standardization, **kwargs)
[docs]
def apply(self, endog, k_endog_monthly=None, endog_quarterly=None,
refit=False, fit_kwargs=None, copy_initialization=False,
retain_standardization=True, **kwargs):
"""
Apply the fitted parameters to new data unrelated to the original data.
Creates a new result object using the current fitted parameters,
applied to a completely new dataset that is assumed to be unrelated to
the model's original data. The new results can then be used for
analysis or forecasting.
Parameters
----------
endog : array_like
New observations from the modeled time-series process.
k_endog_monthly : int, optional
If specifying a monthly/quarterly mixed frequency model in which
the provided `endog` dataset contains both the monthly and
quarterly data, this variable should be used to indicate how many
of the variables are monthly.
endog_quarterly : array_like, optional
New observations of quarterly variables. If provided, must be a
Pandas Series or DataFrame with a DatetimeIndex or PeriodIndex at
the quarterly frequency.
refit : bool, optional
Whether to re-fit the parameters, using the new dataset.
Default is False (so parameters from the current results object
are used to create the new results object).
fit_kwargs : dict, optional
Keyword arguments to pass to `fit` (if `refit=True`) or `filter` /
`smooth`.
copy_initialization : bool, optional
Whether or not to copy the initialization from the current results
set to the new model. Default is False.
retain_standardization : bool, optional
Whether or not to use the mean and standard deviations that were
used to standardize the data in the current model in the new model.
Default is True.
**kwargs
Keyword arguments may be used to modify model specification
arguments when created the new model object.
Returns
-------
results
Updated Results object, that includes results only for the new
dataset.
See Also
--------
statsmodels.tsa.statespace.mlemodel.MLEResults.append
statsmodels.tsa.statespace.mlemodel.MLEResults.apply
Notes
-----
The `endog` argument to this method should consist of new observations
that are not necessarily related to the original model's `endog`
dataset. For observations that continue that original dataset by follow
directly after its last element, see the `append` and `extend` methods.
"""
mod = self.model.clone(endog, k_endog_monthly=k_endog_monthly,
endog_quarterly=endog_quarterly,
retain_standardization=retain_standardization,
**kwargs)
if copy_initialization:
init = initialization.Initialization.from_results(
self.filter_results)
mod.ssm.initialization = init
res = self._apply(mod, refit=refit, fit_kwargs=fit_kwargs)
return res
[docs]
def summary(self, alpha=.05, start=None, title=None, model_name=None,
display_params=True, display_diagnostics=False,
display_params_as_list=False, truncate_endog_names=None,
display_max_endog=3):
"""
Summarize the Model.
Parameters
----------
alpha : float, optional
Significance level for the confidence intervals. Default is 0.05.
start : int, optional
Integer of the start observation. Default is 0.
title : str, optional
The title used for the summary table.
model_name : str, optional
The name of the model used. Default is to use model class name.
Returns
-------
summary : Summary instance
This holds the summary table and text, which can be printed or
converted to various output formats.
See Also
--------
statsmodels.iolib.summary.Summary
"""
mod = self.model
# Default title / model name
if title is None:
title = 'Dynamic Factor Results'
if model_name is None:
model_name = self.model._model_name
# Get endog names
endog_names = self.model._get_endog_names(
truncate=truncate_endog_names)
# Get extra elements for top summary table
extra_top_left = None
extra_top_right = []
mle_retvals = getattr(self, 'mle_retvals', None)
mle_settings = getattr(self, 'mle_settings', None)
if mle_settings is not None and mle_settings.method == 'em':
extra_top_right += [('EM Iterations', [f'{mle_retvals.iter}'])]
# Get the basic summary tables
summary = super().summary(
alpha=alpha, start=start, title=title, model_name=model_name,
display_params=(display_params and display_params_as_list),
display_diagnostics=display_diagnostics,
truncate_endog_names=truncate_endog_names,
display_max_endog=display_max_endog,
extra_top_left=extra_top_left, extra_top_right=extra_top_right)
# Get tables of parameters
table_ix = 1
if not display_params_as_list:
# Observation equation table
data = pd.DataFrame(
self.filter_results.design[:, mod._s['factors_L1'], 0],
index=endog_names, columns=mod.factor_names)
try:
data = data.map(lambda s: '%.2f' % s)
except AttributeError:
data = data.applymap(lambda s: '%.2f' % s)
# Idiosyncratic terms
# data[' '] = ' '
k_idio = 1
if mod.idiosyncratic_ar1:
data[' idiosyncratic: AR(1)'] = (
self.params[mod._p['idiosyncratic_ar1']])
k_idio += 1
data['var.'] = self.params[mod._p['idiosyncratic_var']]
# Ensure object dtype for string assignment
cols_to_cast = data.columns[-k_idio:]
data[cols_to_cast] = data[cols_to_cast].astype(object)
try:
data.iloc[:, -k_idio:] = data.iloc[:, -k_idio:].map(
lambda s: f'{s:.2f}')
except AttributeError:
data.iloc[:, -k_idio:] = data.iloc[:, -k_idio:].applymap(
lambda s: f'{s:.2f}')
data.index.name = 'Factor loadings:'
# Clear entries for non-loading factors
base_iloc = np.arange(mod.k_factors)
for i in range(mod.k_endog):
iloc = [j for j in base_iloc
if j not in mod._s.endog_factor_iloc[i]]
data.iloc[i, iloc] = '.'
data = data.reset_index()
# Build the table
params_data = data.values
params_header = data.columns.tolist()
params_stubs = None
title = 'Observation equation:'
table = SimpleTable(
params_data, params_header, params_stubs,
txt_fmt=fmt_params, title=title)
summary.tables.insert(table_ix, table)
table_ix += 1
# Factor transitions
ix1 = 0
ix2 = 0
for i in range(len(mod._s.factor_blocks)):
block = mod._s.factor_blocks[i]
ix2 += block.k_factors
T = self.filter_results.transition
lag_names = []
for j in range(block.factor_order):
lag_names += [f'L{j + 1}.{name}'
for name in block.factor_names]
data = pd.DataFrame(T[block.factors_L1, block.factors_ar, 0],
index=block.factor_names,
columns=lag_names)
data.index.name = ''
try:
data = data.map(lambda s: '%.2f' % s)
except AttributeError:
data = data.applymap(lambda s: '%.2f' % s)
Q = self.filter_results.state_cov
# data[' '] = ''
if block.k_factors == 1:
data[' error variance'] = Q[ix1, ix1]
else:
data[' error covariance'] = block.factor_names
for j in range(block.k_factors):
data[block.factor_names[j]] = Q[ix1:ix2, ix1 + j]
cols_to_cast = data.columns[-block.k_factors:]
data[cols_to_cast] = data[cols_to_cast].astype(object)
try:
formatted_vals = data.iloc[:, -block.k_factors:].map(
lambda s: f'{s:.2f}'
)
except AttributeError:
formatted_vals = data.iloc[:, -block.k_factors:].applymap(
lambda s: f'{s:.2f}'
)
data.iloc[:, -block.k_factors:] = formatted_vals
data = data.reset_index()
params_data = data.values
params_header = data.columns.tolist()
params_stubs = None
title = f'Transition: Factor block {i}'
table = SimpleTable(
params_data, params_header, params_stubs,
txt_fmt=fmt_params, title=title)
summary.tables.insert(table_ix, table)
table_ix += 1
ix1 = ix2
return summary
Last update:
Nov 14, 2024