Source code for statsmodels.regression.recursive_ls
"""
Recursive least squares model
Author: Chad Fulton
License: Simplified-BSD
"""
import numpy as np
import pandas as pd
from statsmodels.compat.pandas import Appender
from statsmodels.tools.data import _is_using_pandas
from statsmodels.tsa.statespace.mlemodel import (
MLEModel, MLEResults, MLEResultsWrapper, PredictionResults,
PredictionResultsWrapper)
from statsmodels.tsa.statespace.tools import concat
from statsmodels.tools.tools import Bunch
from statsmodels.tools.decorators import cache_readonly
import statsmodels.base.wrapper as wrap
# Columns are alpha = 0.1, 0.05, 0.025, 0.01, 0.005
_cusum_squares_scalars = np.array([
[1.0729830, 1.2238734, 1.3581015, 1.5174271, 1.6276236],
[-0.6698868, -0.6700069, -0.6701218, -0.6702672, -0.6703724],
[-0.5816458, -0.7351697, -0.8858694, -1.0847745, -1.2365861]
])
[docs]
class RecursiveLS(MLEModel):
r"""
Recursive least squares
Parameters
----------
endog : array_like
The observed time-series process :math:`y`
exog : array_like
Array of exogenous regressors, shaped nobs x k.
constraints : array_like, str, or tuple
- array : An r x k array where r is the number of restrictions to
test and k is the number of regressors. It is assumed that the
linear combination is equal to zero.
- str : The full hypotheses to test can be given as a string.
See the examples.
- tuple : A tuple of arrays in the form (R, q), ``q`` can be
either a scalar or a length p row vector.
Notes
-----
Recursive least squares (RLS) corresponds to expanding window ordinary
least squares (OLS).
This model applies the Kalman filter to compute recursive estimates of the
coefficients and recursive residuals.
References
----------
.. [*] Durbin, James, and Siem Jan Koopman. 2012.
Time Series Analysis by State Space Methods: Second Edition.
Oxford University Press.
"""
def __init__(self, endog, exog, constraints=None, **kwargs):
# Standardize data
endog_using_pandas = _is_using_pandas(endog, None)
if not endog_using_pandas:
endog = np.asanyarray(endog)
exog_is_using_pandas = _is_using_pandas(exog, None)
if not exog_is_using_pandas:
exog = np.asarray(exog)
# Make sure we have 2-dimensional array
if exog.ndim == 1:
if not exog_is_using_pandas:
exog = exog[:, None]
else:
exog = pd.DataFrame(exog)
self.k_exog = exog.shape[1]
# Handle constraints
self.k_constraints = 0
self._r_matrix = self._q_matrix = None
if constraints is not None:
from patsy import DesignInfo
from statsmodels.base.data import handle_data
data = handle_data(endog, exog, **kwargs)
names = data.param_names
LC = DesignInfo(names).linear_constraint(constraints)
self._r_matrix, self._q_matrix = LC.coefs, LC.constants
self.k_constraints = self._r_matrix.shape[0]
nobs = len(endog)
constraint_endog = np.zeros((nobs, len(self._r_matrix)))
if endog_using_pandas:
constraint_endog = pd.DataFrame(constraint_endog,
index=endog.index)
endog = concat([endog, constraint_endog], axis=1)
# Complexity needed to handle multiple version of pandas
# Pandas >= 2 can use endog.iloc[:, 1:] = self._q_matrix.T
endog.iloc[:, 1:] = np.tile(self._q_matrix.T, (nobs, 1))
else:
endog[:, 1:] = self._q_matrix[:, 0]
# Handle coefficient initialization
kwargs.setdefault('initialization', 'diffuse')
# Remove some formula-specific kwargs
formula_kwargs = ['missing', 'missing_idx', 'formula', 'design_info']
for name in formula_kwargs:
if name in kwargs:
del kwargs[name]
# Initialize the state space representation
super().__init__(
endog, k_states=self.k_exog, exog=exog, **kwargs)
# Use univariate filtering by default
self.ssm.filter_univariate = True
# Concentrate the scale out of the likelihood function
self.ssm.filter_concentrated = True
# Setup the state space representation
self['design'] = np.zeros((self.k_endog, self.k_states, self.nobs))
self['design', 0] = self.exog[:, :, None].T
if self._r_matrix is not None:
self['design', 1:, :] = self._r_matrix[:, :, None]
self['transition'] = np.eye(self.k_states)
# Notice that the filter output does not depend on the measurement
# variance, so we set it here to 1
self['obs_cov', 0, 0] = 1.
self['transition'] = np.eye(self.k_states)
# Linear constraints are technically imposed by adding "fake" endog
# variables that are used during filtering, but for all model- and
# results-based purposes we want k_endog = 1.
if self._r_matrix is not None:
self.k_endog = 1
[docs]
@classmethod
def from_formula(cls, formula, data, subset=None, constraints=None):
return super(MLEModel, cls).from_formula(formula, data, subset,
constraints=constraints)
def _validate_can_fix_params(self, param_names):
raise ValueError('Linear constraints on coefficients should be given'
' using the `constraints` argument in constructing.'
' the model. Other parameter constraints are not'
' available in the resursive least squares model.')
[docs]
def fit(self):
"""
Fits the model by application of the Kalman filter
Returns
-------
RecursiveLSResults
"""
smoother_results = self.smooth(return_ssm=True)
with self.ssm.fixed_scale(smoother_results.scale):
res = self.smooth()
return res
[docs]
def filter(self, return_ssm=False, **kwargs):
# Get the state space output
result = super().filter([], transformed=True,
cov_type='none',
return_ssm=True, **kwargs)
# Wrap in a results object
if not return_ssm:
params = result.filtered_state[:, -1]
cov_kwds = {
'custom_cov_type': 'nonrobust',
'custom_cov_params': result.filtered_state_cov[:, :, -1],
'custom_description': ('Parameters and covariance matrix'
' estimates are RLS estimates'
' conditional on the entire sample.')
}
result = RecursiveLSResultsWrapper(
RecursiveLSResults(self, params, result, cov_type='custom',
cov_kwds=cov_kwds)
)
return result
[docs]
def smooth(self, return_ssm=False, **kwargs):
# Get the state space output
result = super().smooth([], transformed=True,
cov_type='none',
return_ssm=True, **kwargs)
# Wrap in a results object
if not return_ssm:
params = result.filtered_state[:, -1]
cov_kwds = {
'custom_cov_type': 'nonrobust',
'custom_cov_params': result.filtered_state_cov[:, :, -1],
'custom_description': ('Parameters and covariance matrix'
' estimates are RLS estimates'
' conditional on the entire sample.')
}
result = RecursiveLSResultsWrapper(
RecursiveLSResults(self, params, result, cov_type='custom',
cov_kwds=cov_kwds)
)
return result
@property
def endog_names(self):
endog_names = super().endog_names
return endog_names[0] if isinstance(endog_names, list) else endog_names
@property
def param_names(self):
return self.exog_names
@property
def start_params(self):
# Only parameter is the measurement disturbance standard deviation
return np.zeros(0)
[docs]
def update(self, params, **kwargs):
"""
Update the parameters of the model
Updates the representation matrices to fill in the new parameter
values.
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..
Returns
-------
params : array_like
Array of parameters.
"""
pass
[docs]
class RecursiveLSResults(MLEResults):
"""
Class to hold results from fitting a recursive least squares model.
Parameters
----------
model : RecursiveLS instance
The fitted model instance
Attributes
----------
specification : dictionary
Dictionary including all attributes from the recursive least squares
model instance.
See Also
--------
statsmodels.tsa.statespace.kalman_filter.FilterResults
statsmodels.tsa.statespace.mlemodel.MLEResults
"""
def __init__(self, model, params, filter_results, cov_type='opg',
**kwargs):
super().__init__(
model, params, filter_results, cov_type, **kwargs)
# Since we are overriding params with things that are not MLE params,
# need to adjust df's
q = max(self.loglikelihood_burn, self.k_diffuse_states)
self.df_model = q - self.model.k_constraints
self.df_resid = self.nobs_effective - self.df_model
# Save _init_kwds
self._init_kwds = self.model._get_init_kwds()
# Save the model specification
self.specification = Bunch(**{
'k_exog': self.model.k_exog,
'k_constraints': self.model.k_constraints})
# Adjust results to remove "faux" endog from the constraints
if self.model._r_matrix is not None:
for name in ['forecasts', 'forecasts_error',
'forecasts_error_cov', 'standardized_forecasts_error',
'forecasts_error_diffuse_cov']:
setattr(self, name, getattr(self, name)[0:1])
@property
def recursive_coefficients(self):
"""
Estimates of regression coefficients, recursively estimated
Returns
-------
out: Bunch
Has the following attributes:
- `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
spec = self.specification
start = offset = 0
end = offset + spec.k_exog
out = Bunch(
filtered=self.filtered_state[start:end],
filtered_cov=self.filtered_state_cov[start:end, start:end],
smoothed=None, smoothed_cov=None,
offset=offset
)
if self.smoothed_state is not None:
out.smoothed = self.smoothed_state[start:end]
if self.smoothed_state_cov is not None:
out.smoothed_cov = (
self.smoothed_state_cov[start:end, start:end])
return out
@cache_readonly
def resid_recursive(self):
r"""
Recursive residuals
Returns
-------
resid_recursive : array_like
An array of length `nobs` holding the recursive
residuals.
Notes
-----
These quantities are defined in, for example, Harvey (1989)
section 5.4. In fact, there he defines the standardized innovations in
equation 5.4.1, but in his version they have non-unit variance, whereas
the standardized forecast errors computed by the Kalman filter here
assume unit variance. To convert to Harvey's definition, we need to
multiply by the standard deviation.
Harvey notes that in smaller samples, "although the second moment
of the :math:`\tilde \sigma_*^{-1} \tilde v_t`'s is unity, the
variance is not necessarily equal to unity as the mean need not be
equal to zero", and he defines an alternative version (which are
not provided here).
"""
return (self.filter_results.standardized_forecasts_error[0] *
self.scale**0.5)
@cache_readonly
def cusum(self):
r"""
Cumulative sum of standardized recursive residuals statistics
Returns
-------
cusum : array_like
An array of length `nobs - k_exog` holding the
CUSUM statistics.
Notes
-----
The CUSUM statistic takes the form:
.. math::
W_t = \frac{1}{\hat \sigma} \sum_{j=k+1}^t w_j
where :math:`w_j` is the recursive residual at time :math:`j` and
:math:`\hat \sigma` is the estimate of the standard deviation
from the full sample.
Excludes the first `k_exog` datapoints.
Due to differences in the way :math:`\hat \sigma` is calculated, the
output of this function differs slightly from the output in the
R package strucchange and the Stata contributed .ado file cusum6. The
calculation in this package is consistent with the description of
Brown et al. (1975)
References
----------
.. [*] Brown, R. L., J. Durbin, and J. M. Evans. 1975.
"Techniques for Testing the Constancy of
Regression Relationships over Time."
Journal of the Royal Statistical Society.
Series B (Methodological) 37 (2): 149-92.
"""
d = max(self.nobs_diffuse, self.loglikelihood_burn)
return (np.cumsum(self.resid_recursive[d:]) /
np.std(self.resid_recursive[d:], ddof=1))
@cache_readonly
def cusum_squares(self):
r"""
Cumulative sum of squares of standardized recursive residuals
statistics
Returns
-------
cusum_squares : array_like
An array of length `nobs - k_exog` holding the
CUSUM of squares statistics.
Notes
-----
The CUSUM of squares statistic takes the form:
.. math::
s_t = \left ( \sum_{j=k+1}^t w_j^2 \right ) \Bigg /
\left ( \sum_{j=k+1}^T w_j^2 \right )
where :math:`w_j` is the recursive residual at time :math:`j`.
Excludes the first `k_exog` datapoints.
References
----------
.. [*] Brown, R. L., J. Durbin, and J. M. Evans. 1975.
"Techniques for Testing the Constancy of
Regression Relationships over Time."
Journal of the Royal Statistical Society.
Series B (Methodological) 37 (2): 149-92.
"""
d = max(self.nobs_diffuse, self.loglikelihood_burn)
numer = np.cumsum(self.resid_recursive[d:]**2)
denom = numer[-1]
return numer / denom
@cache_readonly
def llf_recursive_obs(self):
"""
(float) Loglikelihood at observation, computed from recursive residuals
"""
from scipy.stats import norm
return np.log(norm.pdf(self.resid_recursive, loc=0,
scale=self.scale**0.5))
@cache_readonly
def llf_recursive(self):
"""
(float) Loglikelihood defined by recursive residuals, equivalent to OLS
"""
return np.sum(self.llf_recursive_obs)
@cache_readonly
def ssr(self):
"""ssr"""
d = max(self.nobs_diffuse, self.loglikelihood_burn)
return (self.nobs - d) * self.filter_results.obs_cov[0, 0, 0]
@cache_readonly
def centered_tss(self):
"""Centered tss"""
return np.sum((self.filter_results.endog[0] -
np.mean(self.filter_results.endog))**2)
@cache_readonly
def uncentered_tss(self):
"""uncentered tss"""
return np.sum((self.filter_results.endog[0])**2)
@cache_readonly
def ess(self):
"""ess"""
if self.k_constant:
return self.centered_tss - self.ssr
else:
return self.uncentered_tss - self.ssr
@cache_readonly
def rsquared(self):
"""rsquared"""
if self.k_constant:
return 1 - self.ssr / self.centered_tss
else:
return 1 - self.ssr / self.uncentered_tss
@cache_readonly
def mse_model(self):
"""mse_model"""
return self.ess / self.df_model
@cache_readonly
def mse_resid(self):
"""mse_resid"""
return self.ssr / self.df_resid
@cache_readonly
def mse_total(self):
"""mse_total"""
if self.k_constant:
return self.centered_tss / (self.df_resid + self.df_model)
else:
return self.uncentered_tss / (self.df_resid + self.df_model)
[docs]
@Appender(MLEResults.get_prediction.__doc__)
def get_prediction(self, start=None, end=None, dynamic=False,
information_set='predicted', signal_only=False,
index=None, **kwargs):
# Note: need to override this, because we currently do not support
# dynamic prediction or forecasts when there are constraints.
if start is None:
start = self.model._index[0]
# Handle start, end, dynamic
start, end, out_of_sample, prediction_index = (
self.model._get_prediction_index(start, end, index))
# Handle `dynamic`
if isinstance(dynamic, (bytes, str)):
dynamic, _, _ = self.model._get_index_loc(dynamic)
if self.model._r_matrix is not None and (out_of_sample or dynamic):
raise NotImplementedError('Cannot yet perform out-of-sample or'
' dynamic prediction in models with'
' constraints.')
# Perform the prediction
# This is a (k_endog x npredictions) array; do not want to squeeze in
# case of npredictions = 1
prediction_results = self.filter_results.predict(
start, end + out_of_sample + 1, dynamic, **kwargs)
# Return a new mlemodel.PredictionResults object
res_obj = PredictionResults(self, prediction_results,
information_set=information_set,
signal_only=signal_only,
row_labels=prediction_index)
return PredictionResultsWrapper(res_obj)
[docs]
def plot_recursive_coefficient(self, variables=0, alpha=0.05,
legend_loc='upper left', fig=None,
figsize=None):
r"""
Plot the recursively estimated coefficients on a given variable
Parameters
----------
variables : {int, str, list[int], list[str]}, optional
Integer index or string name of the variable whose coefficient will
be plotted. Can also be an iterable of integers or strings. Default
is the first variable.
alpha : float, optional
The confidence intervals for the coefficient are (1 - alpha) %
legend_loc : str, optional
The location of the legend in the plot. Default is upper left.
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
-----
All plots contain (1 - `alpha`) % confidence intervals.
"""
# Get variables
if isinstance(variables, (int, str)):
variables = [variables]
k_variables = len(variables)
# If a string was given for `variable`, try to get it from exog names
exog_names = self.model.exog_names
for i in range(k_variables):
variable = variables[i]
if isinstance(variable, str):
variables[i] = exog_names.index(variable)
# Create the plot
from scipy.stats import norm
from statsmodels.graphics.utils import _import_mpl, create_mpl_fig
plt = _import_mpl()
fig = create_mpl_fig(fig, figsize)
for i in range(k_variables):
variable = variables[i]
ax = fig.add_subplot(k_variables, 1, i + 1)
# Get dates, if applicable
if hasattr(self.data, 'dates') and self.data.dates is not None:
dates = self.data.dates._mpl_repr()
else:
dates = np.arange(self.nobs)
d = max(self.nobs_diffuse, self.loglikelihood_burn)
# Plot the coefficient
coef = self.recursive_coefficients
ax.plot(dates[d:], coef.filtered[variable, d:],
label='Recursive estimates: %s' % exog_names[variable])
# Legend
handles, labels = ax.get_legend_handles_labels()
# Get the critical value for confidence intervals
if alpha is not None:
critical_value = norm.ppf(1 - alpha / 2.)
# Plot confidence intervals
std_errors = np.sqrt(coef.filtered_cov[variable, variable, :])
ci_lower = (
coef.filtered[variable] - critical_value * std_errors)
ci_upper = (
coef.filtered[variable] + critical_value * std_errors)
ci_poly = ax.fill_between(
dates[d:], ci_lower[d:], ci_upper[d:], alpha=0.2
)
ci_label = ('$%.3g \\%%$ confidence interval'
% ((1 - alpha)*100))
# Only add CI to legend for the first plot
if i == 0:
# Proxy artist for fill_between legend entry
# See https://matplotlib.org/1.3.1/users/legend_guide.html
p = plt.Rectangle((0, 0), 1, 1,
fc=ci_poly.get_facecolor()[0])
handles.append(p)
labels.append(ci_label)
ax.legend(handles, labels, loc=legend_loc)
# Remove xticks for all but the last plot
if i < k_variables - 1:
ax.xaxis.set_ticklabels([])
fig.tight_layout()
return fig
def _cusum_significance_bounds(self, alpha, ddof=0, points=None):
"""
Parameters
----------
alpha : float, optional
The significance bound is alpha %.
ddof : int, optional
The number of periods additional to `k_exog` to exclude in
constructing the bounds. Default is zero. This is usually used
only for testing purposes.
points : iterable, optional
The points at which to evaluate the significance bounds. Default is
two points, beginning and end of the sample.
Notes
-----
Comparing against the cusum6 package for Stata, this does not produce
exactly the same confidence bands (which are produced in cusum6 by
lw, uw) because they burn the first k_exog + 1 periods instead of the
first k_exog. If this change is performed
(so that `tmp = (self.nobs - d - 1)**0.5`), then the output here
matches cusum6.
The cusum6 behavior does not seem to be consistent with
Brown et al. (1975); it is likely they did that because they needed
three initial observations to get the initial OLS estimates, whereas
we do not need to do that.
"""
# Get the constant associated with the significance level
if alpha == 0.01:
scalar = 1.143
elif alpha == 0.05:
scalar = 0.948
elif alpha == 0.10:
scalar = 0.950
else:
raise ValueError('Invalid significance level.')
# Get the points for the significance bound lines
d = max(self.nobs_diffuse, self.loglikelihood_burn)
tmp = (self.nobs - d - ddof)**0.5
def upper_line(x):
return scalar * tmp + 2 * scalar * (x - d) / tmp
if points is None:
points = np.array([d, self.nobs])
return -upper_line(points), upper_line(points)
[docs]
def plot_cusum(self, alpha=0.05, legend_loc='upper left',
fig=None, figsize=None):
r"""
Plot the CUSUM statistic and significance bounds.
Parameters
----------
alpha : float, optional
The plotted significance bounds are alpha %.
legend_loc : str, optional
The location of the legend in the plot. Default is upper left.
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
-----
Evidence of parameter instability may be found if the CUSUM statistic
moves out of the significance bounds.
References
----------
.. [*] Brown, R. L., J. Durbin, and J. M. Evans. 1975.
"Techniques for Testing the Constancy of
Regression Relationships over Time."
Journal of the Royal Statistical Society.
Series B (Methodological) 37 (2): 149-92.
"""
# Create the plot
from statsmodels.graphics.utils import _import_mpl, create_mpl_fig
_import_mpl()
fig = create_mpl_fig(fig, figsize)
ax = fig.add_subplot(1, 1, 1)
# Get dates, if applicable
if hasattr(self.data, 'dates') and self.data.dates is not None:
dates = self.data.dates._mpl_repr()
else:
dates = np.arange(self.nobs)
d = max(self.nobs_diffuse, self.loglikelihood_burn)
# Plot cusum series and reference line
ax.plot(dates[d:], self.cusum, label='CUSUM')
ax.hlines(0, dates[d], dates[-1], color='k', alpha=0.3)
# Plot significance bounds
lower_line, upper_line = self._cusum_significance_bounds(alpha)
ax.plot([dates[d], dates[-1]], upper_line, 'k--',
label='%d%% significance' % (alpha * 100))
ax.plot([dates[d], dates[-1]], lower_line, 'k--')
ax.legend(loc=legend_loc)
return fig
def _cusum_squares_significance_bounds(self, alpha, points=None):
"""
Notes
-----
Comparing against the cusum6 package for Stata, this does not produce
exactly the same confidence bands (which are produced in cusum6 by
lww, uww) because they use a different method for computing the
critical value; in particular, they use tabled values from
Table C, pp. 364-365 of "The Econometric Analysis of Time Series"
Harvey, (1990), and use the value given to 99 observations for any
larger number of observations. In contrast, we use the approximating
critical values suggested in Edgerton and Wells (1994) which allows
computing relatively good approximations for any number of
observations.
"""
# Get the approximate critical value associated with the significance
# level
d = max(self.nobs_diffuse, self.loglikelihood_burn)
n = 0.5 * (self.nobs - d) - 1
try:
ix = [0.1, 0.05, 0.025, 0.01, 0.005].index(alpha / 2)
except ValueError:
raise ValueError('Invalid significance level.')
scalars = _cusum_squares_scalars[:, ix]
crit = scalars[0] / n**0.5 + scalars[1] / n + scalars[2] / n**1.5
# Get the points for the significance bound lines
if points is None:
points = np.array([d, self.nobs])
line = (points - d) / (self.nobs - d)
return line - crit, line + crit
[docs]
def plot_cusum_squares(self, alpha=0.05, legend_loc='upper left',
fig=None, figsize=None):
r"""
Plot the CUSUM of squares statistic and significance bounds.
Parameters
----------
alpha : float, optional
The plotted significance bounds are alpha %.
legend_loc : str, optional
The location of the legend in the plot. Default is upper left.
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
-----
Evidence of parameter instability may be found if the CUSUM of squares
statistic moves out of the significance bounds.
Critical values used in creating the significance bounds are computed
using the approximate formula of [1]_.
References
----------
.. [*] Brown, R. L., J. Durbin, and J. M. Evans. 1975.
"Techniques for Testing the Constancy of
Regression Relationships over Time."
Journal of the Royal Statistical Society.
Series B (Methodological) 37 (2): 149-92.
.. [1] Edgerton, David, and Curt Wells. 1994.
"Critical Values for the Cusumsq Statistic
in Medium and Large Sized Samples."
Oxford Bulletin of Economics and Statistics 56 (3): 355-65.
"""
# Create the plot
from statsmodels.graphics.utils import _import_mpl, create_mpl_fig
_import_mpl()
fig = create_mpl_fig(fig, figsize)
ax = fig.add_subplot(1, 1, 1)
# Get dates, if applicable
if hasattr(self.data, 'dates') and self.data.dates is not None:
dates = self.data.dates._mpl_repr()
else:
dates = np.arange(self.nobs)
d = max(self.nobs_diffuse, self.loglikelihood_burn)
# Plot cusum series and reference line
ax.plot(dates[d:], self.cusum_squares, label='CUSUM of squares')
ref_line = (np.arange(d, self.nobs) - d) / (self.nobs - d)
ax.plot(dates[d:], ref_line, 'k', alpha=0.3)
# Plot significance bounds
lower_line, upper_line = self._cusum_squares_significance_bounds(alpha)
ax.plot([dates[d], dates[-1]], upper_line, 'k--',
label='%d%% significance' % (alpha * 100))
ax.plot([dates[d], dates[-1]], lower_line, 'k--')
ax.legend(loc=legend_loc)
return fig
class RecursiveLSResultsWrapper(MLEResultsWrapper):
_attrs = {}
_wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(RecursiveLSResultsWrapper, # noqa:E305
RecursiveLSResults)
Last update:
Nov 14, 2024