Source code for statsmodels.stats.diagnostic
"""
Various Statistical Tests
Author: josef-pktd
License: BSD-3
Notes
-----
Almost fully verified against R or Gretl, not all options are the same.
In many cases of Lagrange multiplier tests both the LM test and the F test is
returned. In some but not all cases, R has the option to choose the test
statistic. Some alternative test statistic results have not been verified.
TODO
* refactor to store intermediate results
missing:
* pvalues for breaks_hansen
* additional options, compare with R, check where ddof is appropriate
* new tests:
- breaks_ap, more recent breaks tests
- specification tests against nonparametric alternatives
"""
from statsmodels.compat.pandas import deprecate_kwarg
from collections.abc import Iterable
import numpy as np
import pandas as pd
from scipy import stats
from statsmodels.regression.linear_model import OLS, RegressionResultsWrapper
from statsmodels.stats._adnorm import anderson_statistic, normal_ad
from statsmodels.stats._lilliefors import (
kstest_exponential,
kstest_fit,
kstest_normal,
lilliefors,
)
from statsmodels.tools.validation import (
array_like,
bool_like,
dict_like,
float_like,
int_like,
string_like,
)
from statsmodels.tsa.tsatools import lagmat
__all__ = ["kstest_fit", "lilliefors", "kstest_normal", "kstest_exponential",
"normal_ad", "compare_cox", "compare_j", "acorr_breusch_godfrey",
"acorr_ljungbox", "acorr_lm", "het_arch", "het_breuschpagan",
"het_goldfeldquandt", "het_white", "spec_white", "linear_lm",
"linear_rainbow", "linear_harvey_collier", "anderson_statistic"]
NESTED_ERROR = """\
The exog in results_x and in results_z are nested. {test} requires \
that models are non-nested.
"""
def _check_nested_exog(small, large):
"""
Check if a larger exog nests a smaller exog
Parameters
----------
small : ndarray
exog from smaller model
large : ndarray
exog from larger model
Returns
-------
bool
True if small is nested by large
"""
if small.shape[1] > large.shape[1]:
return False
coef = np.linalg.lstsq(large, small, rcond=None)[0]
err = small - large @ coef
return np.linalg.matrix_rank(np.c_[large, err]) == large.shape[1]
def _check_nested_results(results_x, results_z):
if not isinstance(results_x, RegressionResultsWrapper):
raise TypeError("results_x must come from a linear regression model")
if not isinstance(results_z, RegressionResultsWrapper):
raise TypeError("results_z must come from a linear regression model")
if not np.allclose(results_x.model.endog, results_z.model.endog):
raise ValueError("endogenous variables in models are not the same")
x = results_x.model.exog
z = results_z.model.exog
nested = False
if x.shape[1] <= z.shape[1]:
nested = nested or _check_nested_exog(x, z)
else:
nested = nested or _check_nested_exog(z, x)
return nested
class ResultsStore:
def __str__(self):
return getattr(self, '_str', self.__class__.__name__)
[docs]
def compare_cox(results_x, results_z, store=False):
"""
Compute the Cox test for non-nested models
Parameters
----------
results_x : Result instance
result instance of first model
results_z : Result instance
result instance of second model
store : bool, default False
If true, then the intermediate results are returned.
Returns
-------
tstat : float
t statistic for the test that including the fitted values of the
first model in the second model has no effect.
pvalue : float
two-sided pvalue for the t statistic
res_store : ResultsStore, optional
Intermediate results. Returned if store is True.
Notes
-----
Tests of non-nested hypothesis might not provide unambiguous answers.
The test should be performed in both directions and it is possible
that both or neither test rejects. see [1]_ for more information.
Formulas from [1]_, section 8.3.4 translated to code
Matches results for Example 8.3 in Greene
References
----------
.. [1] Greene, W. H. Econometric Analysis. New Jersey. Prentice Hall;
5th edition. (2002).
"""
if _check_nested_results(results_x, results_z):
raise ValueError(NESTED_ERROR.format(test="Cox comparison"))
x = results_x.model.exog
z = results_z.model.exog
nobs = results_x.model.endog.shape[0]
sigma2_x = results_x.ssr / nobs
sigma2_z = results_z.ssr / nobs
yhat_x = results_x.fittedvalues
res_dx = OLS(yhat_x, z).fit()
err_zx = res_dx.resid
res_xzx = OLS(err_zx, x).fit()
err_xzx = res_xzx.resid
sigma2_zx = sigma2_x + np.dot(err_zx.T, err_zx) / nobs
c01 = nobs / 2. * (np.log(sigma2_z) - np.log(sigma2_zx))
v01 = sigma2_x * np.dot(err_xzx.T, err_xzx) / sigma2_zx ** 2
q = c01 / np.sqrt(v01)
pval = 2 * stats.norm.sf(np.abs(q))
if store:
res = ResultsStore()
res.res_dx = res_dx
res.res_xzx = res_xzx
res.c01 = c01
res.v01 = v01
res.q = q
res.pvalue = pval
res.dist = stats.norm
return q, pval, res
return q, pval
[docs]
def compare_j(results_x, results_z, store=False):
"""
Compute the J-test for non-nested models
Parameters
----------
results_x : RegressionResults
The result instance of first model.
results_z : RegressionResults
The result instance of second model.
store : bool, default False
If true, then the intermediate results are returned.
Returns
-------
tstat : float
t statistic for the test that including the fitted values of the
first model in the second model has no effect.
pvalue : float
two-sided pvalue for the t statistic
res_store : ResultsStore, optional
Intermediate results. Returned if store is True.
Notes
-----
From description in Greene, section 8.3.3. Matches results for Example
8.3, Greene.
Tests of non-nested hypothesis might not provide unambiguous answers.
The test should be performed in both directions and it is possible
that both or neither test rejects. see Greene for more information.
References
----------
.. [1] Greene, W. H. Econometric Analysis. New Jersey. Prentice Hall;
5th edition. (2002).
"""
# TODO: Allow cov to be specified
if _check_nested_results(results_x, results_z):
raise ValueError(NESTED_ERROR.format(test="J comparison"))
y = results_x.model.endog
z = results_z.model.exog
yhat_x = results_x.fittedvalues
res_zx = OLS(y, np.column_stack((yhat_x, z))).fit()
tstat = res_zx.tvalues[0]
pval = res_zx.pvalues[0]
if store:
res = ResultsStore()
res.res_zx = res_zx
res.dist = stats.t(res_zx.df_resid)
res.teststat = tstat
res.pvalue = pval
return tstat, pval, res
return tstat, pval
[docs]
def compare_encompassing(results_x, results_z, cov_type="nonrobust",
cov_kwargs=None):
r"""
Davidson-MacKinnon encompassing test for comparing non-nested models
Parameters
----------
results_x : Result instance
result instance of first model
results_z : Result instance
result instance of second model
cov_type : str, default "nonrobust
Covariance type. The default is "nonrobust` which uses the classic
OLS covariance estimator. Specify one of "HC0", "HC1", "HC2", "HC3"
to use White's covariance estimator. All covariance types supported
by ``OLS.fit`` are accepted.
cov_kwargs : dict, default None
Dictionary of covariance options passed to ``OLS.fit``. See OLS.fit
for more details.
Returns
-------
DataFrame
A DataFrame with two rows and four columns. The row labeled x
contains results for the null that the model contained in
results_x is equivalent to the encompassing model. The results in
the row labeled z correspond to the test that the model contained
in results_z are equivalent to the encompassing model. The columns
are the test statistic, its p-value, and the numerator and
denominator degrees of freedom. The test statistic has an F
distribution. The numerator degree of freedom is the number of
variables in the encompassing model that are not in the x or z model.
The denominator degree of freedom is the number of observations minus
the number of variables in the nesting model.
Notes
-----
The null is that the fit produced using x is the same as the fit
produced using both x and z. When testing whether x is encompassed,
the model estimated is
.. math::
Y = X\beta + Z_1\gamma + \epsilon
where :math:`Z_1` are the columns of :math:`Z` that are not spanned by
:math:`X`. The null is :math:`H_0:\gamma=0`. When testing whether z is
encompassed, the roles of :math:`X` and :math:`Z` are reversed.
Implementation of Davidson and MacKinnon (1993)'s encompassing test.
Performs two Wald tests where models x and z are compared to a model
that nests the two. The Wald tests are performed by using an OLS
regression.
"""
if _check_nested_results(results_x, results_z):
raise ValueError(NESTED_ERROR.format(test="Testing encompassing"))
y = results_x.model.endog
x = results_x.model.exog
z = results_z.model.exog
def _test_nested(endog, a, b, cov_est, cov_kwds):
err = b - a @ np.linalg.lstsq(a, b, rcond=None)[0]
u, s, v = np.linalg.svd(err)
eps = np.finfo(np.double).eps
tol = s.max(axis=-1, keepdims=True) * max(err.shape) * eps
non_zero = np.abs(s) > tol
aug = err @ v[:, non_zero]
aug_reg = np.hstack([a, aug])
k_a = aug.shape[1]
k = aug_reg.shape[1]
res = OLS(endog, aug_reg).fit(cov_type=cov_est, cov_kwds=cov_kwds)
r_matrix = np.zeros((k_a, k))
r_matrix[:, -k_a:] = np.eye(k_a)
test = res.wald_test(r_matrix, use_f=True, scalar=True)
stat, pvalue = test.statistic, test.pvalue
df_num, df_denom = int(test.df_num), int(test.df_denom)
return stat, pvalue, df_num, df_denom
x_nested = _test_nested(y, x, z, cov_type, cov_kwargs)
z_nested = _test_nested(y, z, x, cov_type, cov_kwargs)
return pd.DataFrame([x_nested, z_nested],
index=["x", "z"],
columns=["stat", "pvalue", "df_num", "df_denom"])
[docs]
def acorr_ljungbox(x, lags=None, boxpierce=False, model_df=0, period=None,
return_df=True, auto_lag=False):
"""
Ljung-Box test of autocorrelation in residuals.
Parameters
----------
x : array_like
The data series. The data is demeaned before the test statistic is
computed.
lags : {int, array_like}, default None
If lags is an integer then this is taken to be the largest lag
that is included, the test result is reported for all smaller lag
length. If lags is a list or array, then all lags are included up to
the largest lag in the list, however only the tests for the lags in
the list are reported. If lags is None, then the default maxlag is
min(10, nobs // 5). The default number of lags changes if period
is set.
boxpierce : bool, default False
If true, then additional to the results of the Ljung-Box test also the
Box-Pierce test results are returned.
model_df : int, default 0
Number of degrees of freedom consumed by the model. In an ARMA model,
this value is usually p+q where p is the AR order and q is the MA
order. This value is subtracted from the degrees-of-freedom used in
the test so that the adjusted dof for the statistics are
lags - model_df. If lags - model_df <= 0, then NaN is returned.
period : int, default None
The period of a Seasonal time series. Used to compute the max lag
for seasonal data which uses min(2*period, nobs // 5) if set. If None,
then the default rule is used to set the number of lags. When set, must
be >= 2.
auto_lag : bool, default False
Flag indicating whether to automatically determine the optimal lag
length based on threshold of maximum correlation value.
Returns
-------
DataFrame
Frame with columns:
* lb_stat - The Ljung-Box test statistic.
* lb_pvalue - The p-value based on chi-square distribution. The
p-value is computed as 1 - chi2.cdf(lb_stat, dof) where dof is
lag - model_df. If lag - model_df <= 0, then NaN is returned for
the pvalue.
* bp_stat - The Box-Pierce test statistic.
* bp_pvalue - The p-value based for Box-Pierce test on chi-square
distribution. The p-value is computed as 1 - chi2.cdf(bp_stat, dof)
where dof is lag - model_df. If lag - model_df <= 0, then NaN is
returned for the pvalue.
See Also
--------
statsmodels.regression.linear_model.OLS.fit
Regression model fitting.
statsmodels.regression.linear_model.RegressionResults
Results from linear regression models.
statsmodels.stats.stattools.q_stat
Ljung-Box test statistic computed from estimated
autocorrelations.
Notes
-----
Ljung-Box and Box-Pierce statistic differ in their scaling of the
autocorrelation function. Ljung-Box test is has better finite-sample
properties.
References
----------
.. [*] Green, W. "Econometric Analysis," 5th ed., Pearson, 2003.
.. [*] J. Carlos Escanciano, Ignacio N. Lobato
"An automatic Portmanteau test for serial correlation".,
Volume 151, 2009.
Examples
--------
>>> import statsmodels.api as sm
>>> data = sm.datasets.sunspots.load_pandas().data
>>> res = sm.tsa.ARMA(data["SUNACTIVITY"], (1,1)).fit(disp=-1)
>>> sm.stats.acorr_ljungbox(res.resid, lags=[10], return_df=True)
lb_stat lb_pvalue
10 214.106992 1.827374e-40
"""
# Avoid cyclic import
from statsmodels.tsa.stattools import acf
x = array_like(x, "x")
period = int_like(period, "period", optional=True)
model_df = int_like(model_df, "model_df", optional=False)
if period is not None and period <= 1:
raise ValueError("period must be >= 2")
if model_df < 0:
raise ValueError("model_df must be >= 0")
nobs = x.shape[0]
if auto_lag:
maxlag = nobs - 1
# Compute sum of squared autocorrelations
sacf = acf(x, nlags=maxlag, fft=False)
if not boxpierce:
q_sacf = (nobs * (nobs + 2) *
np.cumsum(sacf[1:maxlag + 1] ** 2
/ (nobs - np.arange(1, maxlag + 1))))
else:
q_sacf = nobs * np.cumsum(sacf[1:maxlag + 1] ** 2)
# obtain thresholds
q = 2.4
threshold = np.sqrt(q * np.log(nobs))
threshold_metric = np.abs(sacf).max() * np.sqrt(nobs)
# compute penalized sum of squared autocorrelations
if (threshold_metric <= threshold):
q_sacf = q_sacf - (np.arange(1, nobs) * np.log(nobs))
else:
q_sacf = q_sacf - (2 * np.arange(1, nobs))
# note: np.argmax returns first (i.e., smallest) index of largest value
lags = np.argmax(q_sacf)
lags = max(1, lags) # optimal lag has to be at least 1
lags = int_like(lags, "lags")
lags = np.arange(1, lags + 1)
elif period is not None:
lags = np.arange(1, min(nobs // 5, 2 * period) + 1, dtype=int)
elif lags is None:
lags = np.arange(1, min(nobs // 5, 10) + 1, dtype=int)
elif not isinstance(lags, Iterable):
lags = int_like(lags, "lags")
lags = np.arange(1, lags + 1)
lags = array_like(lags, "lags", dtype="int")
maxlag = lags.max()
# normalize by nobs not (nobs-nlags)
# SS: unbiased=False is default now
sacf = acf(x, nlags=maxlag, fft=False)
sacf2 = sacf[1:maxlag + 1] ** 2 / (nobs - np.arange(1, maxlag + 1))
qljungbox = nobs * (nobs + 2) * np.cumsum(sacf2)[lags - 1]
adj_lags = lags - model_df
pval = np.full_like(qljungbox, np.nan)
loc = adj_lags > 0
pval[loc] = stats.chi2.sf(qljungbox[loc], adj_lags[loc])
if not boxpierce:
return pd.DataFrame({"lb_stat": qljungbox, "lb_pvalue": pval},
index=lags)
qboxpierce = nobs * np.cumsum(sacf[1:maxlag + 1] ** 2)[lags - 1]
pvalbp = np.full_like(qljungbox, np.nan)
pvalbp[loc] = stats.chi2.sf(qboxpierce[loc], adj_lags[loc])
return pd.DataFrame({"lb_stat": qljungbox, "lb_pvalue": pval,
"bp_stat": qboxpierce, "bp_pvalue": pvalbp},
index=lags)
[docs]
@deprecate_kwarg("maxlag", "nlags")
def acorr_lm(resid, nlags=None, store=False, *, period=None,
ddof=0, cov_type="nonrobust", cov_kwargs=None):
"""
Lagrange Multiplier tests for autocorrelation.
This is a generic Lagrange Multiplier test for autocorrelation. Returns
Engle's ARCH test if resid is the squared residual array. Breusch-Godfrey
is a variation on this test with additional exogenous variables.
Parameters
----------
resid : array_like
Time series to test.
nlags : int, default None
Highest lag to use.
store : bool, default False
If true then the intermediate results are also returned.
period : int, default none
The period of a Seasonal time series. Used to compute the max lag
for seasonal data which uses min(2*period, nobs // 5) if set. If None,
then the default rule is used to set the number of lags. When set, must
be >= 2.
ddof : int, default 0
The number of degrees of freedom consumed by the model used to
produce resid. The default value is 0.
cov_type : str, default "nonrobust"
Covariance type. The default is "nonrobust` which uses the classic
OLS covariance estimator. Specify one of "HC0", "HC1", "HC2", "HC3"
to use White's covariance estimator. All covariance types supported
by ``OLS.fit`` are accepted.
cov_kwargs : dict, default None
Dictionary of covariance options passed to ``OLS.fit``. See OLS.fit for
more details.
Returns
-------
lm : float
Lagrange multiplier test statistic.
lmpval : float
The p-value for Lagrange multiplier test.
fval : float
The f statistic of the F test, alternative version of the same
test based on F test for the parameter restriction.
fpval : float
The pvalue of the F test.
res_store : ResultsStore, optional
Intermediate results. Only returned if store=True.
See Also
--------
het_arch
Conditional heteroskedasticity testing.
acorr_breusch_godfrey
Breusch-Godfrey test for serial correlation.
acorr_ljung_box
Ljung-Box test for serial correlation.
Notes
-----
The test statistic is computed as (nobs - ddof) * r2 where r2 is the
R-squared from a regression on the residual on nlags lags of the
residual.
"""
resid = array_like(resid, "resid", ndim=1)
cov_type = string_like(cov_type, "cov_type")
cov_kwargs = {} if cov_kwargs is None else cov_kwargs
cov_kwargs = dict_like(cov_kwargs, "cov_kwargs")
nobs = resid.shape[0]
if period is not None and nlags is None:
maxlag = min(nobs // 5, 2 * period)
elif nlags is None:
maxlag = min(10, nobs // 5)
else:
maxlag = nlags
xdall = lagmat(resid[:, None], maxlag, trim="both")
nobs = xdall.shape[0]
xdall = np.c_[np.ones((nobs, 1)), xdall]
xshort = resid[-nobs:]
res_store = ResultsStore()
usedlag = maxlag
resols = OLS(xshort, xdall[:, :usedlag + 1]).fit(cov_type=cov_type,
cov_kwargs=cov_kwargs)
fval = float(resols.fvalue)
fpval = float(resols.f_pvalue)
if cov_type == "nonrobust":
lm = (nobs - ddof) * resols.rsquared
lmpval = stats.chi2.sf(lm, usedlag)
# Note: deg of freedom for LM test: nvars - constant = lags used
else:
r_matrix = np.hstack((np.zeros((usedlag, 1)), np.eye(usedlag)))
test_stat = resols.wald_test(r_matrix, use_f=False, scalar=True)
lm = float(test_stat.statistic)
lmpval = float(test_stat.pvalue)
if store:
res_store.resols = resols
res_store.usedlag = usedlag
return lm, lmpval, fval, fpval, res_store
else:
return lm, lmpval, fval, fpval
[docs]
@deprecate_kwarg("maxlag", "nlags")
def het_arch(resid, nlags=None, store=False, ddof=0):
"""
Engle's Test for Autoregressive Conditional Heteroscedasticity (ARCH).
Parameters
----------
resid : ndarray
residuals from an estimation, or time series
nlags : int, default None
Highest lag to use.
store : bool, default False
If true then the intermediate results are also returned
ddof : int, default 0
If the residuals are from a regression, or ARMA estimation, then there
are recommendations to correct the degrees of freedom by the number
of parameters that have been estimated, for example ddof=p+q for an
ARMA(p,q).
Returns
-------
lm : float
Lagrange multiplier test statistic
lmpval : float
p-value for Lagrange multiplier test
fval : float
fstatistic for F test, alternative version of the same test based on
F test for the parameter restriction
fpval : float
pvalue for F test
res_store : ResultsStore, optional
Intermediate results. Returned if store is True.
Notes
-----
verified against R:FinTS::ArchTest
"""
return acorr_lm(resid ** 2, nlags=nlags, store=store, ddof=ddof)
[docs]
@deprecate_kwarg("results", "res")
def acorr_breusch_godfrey(res, nlags=None, store=False):
"""
Breusch-Godfrey Lagrange Multiplier tests for residual autocorrelation.
Parameters
----------
res : RegressionResults
Estimation results for which the residuals are tested for serial
correlation.
nlags : int, optional
Number of lags to include in the auxiliary regression. (nlags is
highest lag).
store : bool, default False
If store is true, then an additional class instance that contains
intermediate results is returned.
Returns
-------
lm : float
Lagrange multiplier test statistic.
lmpval : float
The p-value for Lagrange multiplier test.
fval : float
The value of the f statistic for F test, alternative version of the
same test based on F test for the parameter restriction.
fpval : float
The pvalue for F test.
res_store : ResultsStore
A class instance that holds intermediate results. Only returned if
store=True.
Notes
-----
BG adds lags of residual to exog in the design matrix for the auxiliary
regression with residuals as endog. See [1]_, section 12.7.1.
References
----------
.. [1] Greene, W. H. Econometric Analysis. New Jersey. Prentice Hall;
5th edition. (2002).
"""
x = np.asarray(res.resid).squeeze()
if x.ndim != 1:
raise ValueError("Model resid must be a 1d array. Cannot be used on"
" multivariate models.")
exog_old = res.model.exog
nobs = x.shape[0]
if nlags is None:
nlags = min(10, nobs // 5)
x = np.concatenate((np.zeros(nlags), x))
xdall = lagmat(x[:, None], nlags, trim="both")
nobs = xdall.shape[0]
xdall = np.c_[np.ones((nobs, 1)), xdall]
xshort = x[-nobs:]
if exog_old is None:
exog = xdall
else:
exog = np.column_stack((exog_old, xdall))
k_vars = exog.shape[1]
resols = OLS(xshort, exog).fit()
ft = resols.f_test(np.eye(nlags, k_vars, k_vars - nlags))
fval = ft.fvalue
fpval = ft.pvalue
fval = float(np.squeeze(fval))
fpval = float(np.squeeze(fpval))
lm = nobs * resols.rsquared
lmpval = stats.chi2.sf(lm, nlags)
# Note: degrees of freedom for LM test is nvars minus constant = usedlags
if store:
res_store = ResultsStore()
res_store.resols = resols
res_store.usedlag = nlags
return lm, lmpval, fval, fpval, res_store
else:
return lm, lmpval, fval, fpval
def _check_het_test(x: np.ndarray, test_name: str) -> None:
"""
Check validity of the exogenous regressors in a heteroskedasticity test
Parameters
----------
x : ndarray
The exogenous regressor array
test_name : str
The test name for the exception
"""
x_max = x.max(axis=0)
if (
not np.any(((x_max - x.min(axis=0)) == 0) & (x_max != 0))
or x.shape[1] < 2
):
raise ValueError(
f"{test_name} test requires exog to have at least "
"two columns where one is a constant."
)
[docs]
def het_breuschpagan(resid, exog_het, robust=True):
r"""
Breusch-Pagan Lagrange Multiplier test for heteroscedasticity
The tests the hypothesis that the residual variance does not depend on
the variables in x in the form
.. :math: \sigma_i = \sigma * f(\alpha_0 + \alpha z_i)
Homoscedasticity implies that :math:`\alpha=0`.
Parameters
----------
resid : array_like
For the Breusch-Pagan test, this should be the residual of a
regression. If an array is given in exog, then the residuals are
calculated by the an OLS regression or resid on exog. In this case
resid should contain the dependent variable. Exog can be the same as x.
exog_het : array_like
This contains variables suspected of being related to
heteroscedasticity in resid.
robust : bool, default True
Flag indicating whether to use the Koenker version of the
test (default) which assumes independent and identically distributed
error terms, or the original Breusch-Pagan version which assumes
residuals are normally distributed.
Returns
-------
lm : float
lagrange multiplier statistic
lm_pvalue : float
p-value of lagrange multiplier test
fvalue : float
f-statistic of the hypothesis that the error variance does not depend
on x
f_pvalue : float
p-value for the f-statistic
Notes
-----
Assumes x contains constant (for counting dof and calculation of R^2).
In the general description of LM test, Greene mentions that this test
exaggerates the significance of results in small or moderately large
samples. In this case the F-statistic is preferable.
**Verification**
Chisquare test statistic is exactly (<1e-13) the same result as bptest
in R-stats with defaults (studentize=True).
**Implementation**
This is calculated using the generic formula for LM test using $R^2$
(Greene, section 17.6) and not with the explicit formula
(Greene, section 11.4.3), unless `robust` is set to False.
The degrees of freedom for the p-value assume x is full rank.
References
----------
.. [1] Greene, W. H. Econometric Analysis. New Jersey. Prentice Hall;
5th edition. (2002).
.. [2] Breusch, T. S.; Pagan, A. R. (1979). "A Simple Test for
Heteroskedasticity and Random Coefficient Variation". Econometrica.
47 (5): 1287–1294.
.. [3] Koenker, R. (1981). "A note on studentizing a test for
heteroskedasticity". Journal of Econometrics 17 (1): 107–112.
"""
x = array_like(exog_het, "exog_het", ndim=2)
_check_het_test(x, "The Breusch-Pagan")
y = array_like(resid, "resid", ndim=1) ** 2
if not robust:
y = y / np.mean(y)
nobs, nvars = x.shape
resols = OLS(y, x).fit()
fval = resols.fvalue
fpval = resols.f_pvalue
lm = nobs * resols.rsquared if robust else resols.ess / 2
# Note: degrees of freedom for LM test is nvars minus constant
return lm, stats.chi2.sf(lm, nvars - 1), fval, fpval
[docs]
def het_white(resid, exog):
"""
White's Lagrange Multiplier Test for Heteroscedasticity.
Parameters
----------
resid : array_like
The residuals. The squared residuals are used as the endogenous
variable.
exog : array_like
The explanatory variables for the variance. Squares and interaction
terms are automatically included in the auxiliary regression.
Returns
-------
lm : float
The lagrange multiplier statistic.
lm_pvalue :float
The p-value of lagrange multiplier test.
fvalue : float
The f-statistic of the hypothesis that the error variance does not
depend on x. This is an alternative test variant not the original
LM test.
f_pvalue : float
The p-value for the f-statistic.
Notes
-----
Assumes x contains constant (for counting dof).
question: does f-statistic make sense? constant ?
References
----------
Greene section 11.4.1 5th edition p. 222. Test statistic reproduces
Greene 5th, example 11.3.
"""
x = array_like(exog, "exog", ndim=2)
y = array_like(resid, "resid", ndim=2, shape=(x.shape[0], 1))
_check_het_test(x, "White's heteroskedasticity")
nobs, nvars0 = x.shape
i0, i1 = np.triu_indices(nvars0)
exog = x[:, i0] * x[:, i1]
nobs, nvars = exog.shape
assert nvars == nvars0 * (nvars0 - 1) / 2. + nvars0
resols = OLS(y ** 2, exog).fit()
fval = resols.fvalue
fpval = resols.f_pvalue
lm = nobs * resols.rsquared
# Note: degrees of freedom for LM test is nvars minus constant
# degrees of freedom take possible reduced rank in exog into account
# df_model checks the rank to determine df
# extra calculation that can be removed:
assert resols.df_model == np.linalg.matrix_rank(exog) - 1
lmpval = stats.chi2.sf(lm, resols.df_model)
return lm, lmpval, fval, fpval
[docs]
def het_goldfeldquandt(y, x, idx=None, split=None, drop=None,
alternative="increasing", store=False):
"""
Goldfeld-Quandt homoskedasticity test.
This test examines whether the residual variance is the same in 2
subsamples.
Parameters
----------
y : array_like
endogenous variable
x : array_like
exogenous variable, regressors
idx : int, default None
column index of variable according to which observations are
sorted for the split
split : {int, float}, default None
If an integer, this is the index at which sample is split.
If a float in 0<split<1 then split is interpreted as fraction
of the observations in the first sample. If None, uses nobs//2.
drop : {int, float}, default None
If this is not None, then observation are dropped from the middle
part of the sorted series. If 0<split<1 then split is interpreted
as fraction of the number of observations to be dropped.
Note: Currently, observations are dropped between split and
split+drop, where split and drop are the indices (given by rounding
if specified as fraction). The first sample is [0:split], the
second sample is [split+drop:]
alternative : {"increasing", "decreasing", "two-sided"}
The default is increasing. This specifies the alternative for the
p-value calculation.
store : bool, default False
Flag indicating to return the regression results
Returns
-------
fval : float
value of the F-statistic
pval : float
p-value of the hypothesis that the variance in one subsample is
larger than in the other subsample
ordering : str
The ordering used in the alternative.
res_store : ResultsStore, optional
Storage for the intermediate and final results that are calculated
Notes
-----
The Null hypothesis is that the variance in the two sub-samples are the
same. The alternative hypothesis, can be increasing, i.e. the variance
in the second sample is larger than in the first, or decreasing or
two-sided.
Results are identical R, but the drop option is defined differently.
(sorting by idx not tested yet)
"""
x = np.asarray(x)
y = np.asarray(y) # **2
nobs, nvars = x.shape
if split is None:
split = nobs // 2
elif (0 < split) and (split < 1):
split = int(nobs * split)
if drop is None:
start2 = split
elif (0 < drop) and (drop < 1):
start2 = split + int(nobs * drop)
else:
start2 = split + drop
if idx is not None:
xsortind = np.argsort(x[:, idx])
y = y[xsortind]
x = x[xsortind, :]
resols1 = OLS(y[:split], x[:split]).fit()
resols2 = OLS(y[start2:], x[start2:]).fit()
fval = resols2.mse_resid / resols1.mse_resid
# if fval>1:
if alternative.lower() in ["i", "inc", "increasing"]:
fpval = stats.f.sf(fval, resols1.df_resid, resols2.df_resid)
ordering = "increasing"
elif alternative.lower() in ["d", "dec", "decreasing"]:
fpval = stats.f.sf(1. / fval, resols2.df_resid, resols1.df_resid)
ordering = "decreasing"
elif alternative.lower() in ["2", "2-sided", "two-sided"]:
fpval_sm = stats.f.cdf(fval, resols2.df_resid, resols1.df_resid)
fpval_la = stats.f.sf(fval, resols2.df_resid, resols1.df_resid)
fpval = 2 * min(fpval_sm, fpval_la)
ordering = "two-sided"
else:
raise ValueError("invalid alternative")
if store:
res = ResultsStore()
res.__doc__ = "Test Results for Goldfeld-Quandt test of" \
"heterogeneity"
res.fval = fval
res.fpval = fpval
res.df_fval = (resols2.df_resid, resols1.df_resid)
res.resols1 = resols1
res.resols2 = resols2
res.ordering = ordering
res.split = split
res._str = """\
The Goldfeld-Quandt test for null hypothesis that the variance in the second
subsample is {} than in the first subsample:
F-statistic ={:8.4f} and p-value ={:8.4f}""".format(ordering, fval, fpval)
return fval, fpval, ordering, res
return fval, fpval, ordering
[docs]
@deprecate_kwarg("result", "res")
def linear_reset(res, power=3, test_type="fitted", use_f=False,
cov_type="nonrobust", cov_kwargs=None):
r"""
Ramsey's RESET test for neglected nonlinearity
Parameters
----------
res : RegressionResults
A results instance from a linear regression.
power : {int, List[int]}, default 3
The maximum power to include in the model, if an integer. Includes
powers 2, 3, ..., power. If an list of integers, includes all powers
in the list.
test_type : str, default "fitted"
The type of augmentation to use:
* "fitted" : (default) Augment regressors with powers of fitted values.
* "exog" : Augment exog with powers of exog. Excludes binary
regressors.
* "princomp": Augment exog with powers of first principal component of
exog.
use_f : bool, default False
Flag indicating whether an F-test should be used (True) or a
chi-square test (False).
cov_type : str, default "nonrobust
Covariance type. The default is "nonrobust` which uses the classic
OLS covariance estimator. Specify one of "HC0", "HC1", "HC2", "HC3"
to use White's covariance estimator. All covariance types supported
by ``OLS.fit`` are accepted.
cov_kwargs : dict, default None
Dictionary of covariance options passed to ``OLS.fit``. See OLS.fit
for more details.
Returns
-------
ContrastResults
Test results for Ramsey's Reset test. See notes for implementation
details.
Notes
-----
The RESET test uses an augmented regression of the form
.. math::
Y = X\beta + Z\gamma + \epsilon
where :math:`Z` are a set of regressors that are one of:
* Powers of :math:`X\hat{\beta}` from the original regression.
* Powers of :math:`X`, excluding the constant and binary regressors.
* Powers of the first principal component of :math:`X`. If the
model includes a constant, this column is dropped before computing
the principal component. In either case, the principal component
is extracted from the correlation matrix of remaining columns.
The test is a Wald test of the null :math:`H_0:\gamma=0`. If use_f
is True, then the quadratic-form test statistic is divided by the
number of restrictions and the F distribution is used to compute
the critical value.
"""
if not isinstance(res, RegressionResultsWrapper):
raise TypeError("result must come from a linear regression model")
if bool(res.model.k_constant) and res.model.exog.shape[1] == 1:
raise ValueError("exog contains only a constant column. The RESET "
"test requires exog to have at least 1 "
"non-constant column.")
test_type = string_like(test_type, "test_type",
options=("fitted", "exog", "princomp"))
cov_kwargs = dict_like(cov_kwargs, "cov_kwargs", optional=True)
use_f = bool_like(use_f, "use_f")
if isinstance(power, int):
if power < 2:
raise ValueError("power must be >= 2")
power = np.arange(2, power + 1, dtype=int)
else:
try:
power = np.array(power, dtype=int)
except Exception:
raise ValueError("power must be an integer or list of integers")
if power.ndim != 1 or len(set(power)) != power.shape[0] or \
(power < 2).any():
raise ValueError("power must contains distinct integers all >= 2")
exog = res.model.exog
if test_type == "fitted":
aug = np.asarray(res.fittedvalues)[:, None]
elif test_type == "exog":
# Remove constant and binary
aug = res.model.exog
binary = ((exog == exog.max(axis=0)) | (exog == exog.min(axis=0)))
binary = binary.all(axis=0)
if binary.all():
raise ValueError("Model contains only constant or binary data")
aug = aug[:, ~binary]
else:
from statsmodels.multivariate.pca import PCA
aug = exog
if res.k_constant:
retain = np.arange(aug.shape[1]).tolist()
retain.pop(int(res.model.data.const_idx))
aug = aug[:, retain]
pca = PCA(aug, ncomp=1, standardize=bool(res.k_constant),
demean=bool(res.k_constant), method="nipals")
aug = pca.factors[:, :1]
aug_exog = np.hstack([exog] + [aug ** p for p in power])
mod_class = res.model.__class__
mod = mod_class(res.model.data.endog, aug_exog)
cov_kwargs = {} if cov_kwargs is None else cov_kwargs
res = mod.fit(cov_type=cov_type, cov_kwargs=cov_kwargs)
nrestr = aug_exog.shape[1] - exog.shape[1]
nparams = aug_exog.shape[1]
r_mat = np.eye(nrestr, nparams, k=nparams-nrestr)
return res.wald_test(r_mat, use_f=use_f, scalar=True)
[docs]
def linear_harvey_collier(res, order_by=None, skip=None):
"""
Harvey Collier test for linearity
The Null hypothesis is that the regression is correctly modeled as linear.
Parameters
----------
res : RegressionResults
A results instance from a linear regression.
order_by : array_like, default None
Integer array specifying the order of the residuals. If not provided,
the order of the residuals is not changed. If provided, must have
the same number of observations as the endogenous variable.
skip : int, default None
The number of observations to use for initial OLS, if None then skip is
set equal to the number of regressors (columns in exog).
Returns
-------
tvalue : float
The test statistic, based on ttest_1sample.
pvalue : float
The pvalue of the test.
See Also
--------
statsmodels.stats.diadnostic.recursive_olsresiduals
Recursive OLS residual calculation used in the test.
Notes
-----
This test is a t-test that the mean of the recursive ols residuals is zero.
Calculating the recursive residuals might take some time for large samples.
"""
# I think this has different ddof than
# B.H. Baltagi, Econometrics, 2011, chapter 8
# but it matches Gretl and R:lmtest, pvalue at decimal=13
rr = recursive_olsresiduals(res, skip=skip, alpha=0.95, order_by=order_by)
return stats.ttest_1samp(rr[3][3:], 0)
[docs]
def linear_rainbow(res, frac=0.5, order_by=None, use_distance=False,
center=None):
"""
Rainbow test for linearity
The null hypothesis is the fit of the model using full sample is the same
as using a central subset. The alternative is that the fits are difference.
The rainbow test has power against many different forms of nonlinearity.
Parameters
----------
res : RegressionResults
A results instance from a linear regression.
frac : float, default 0.5
The fraction of the data to include in the center model.
order_by : {ndarray, str, List[str]}, default None
If an ndarray, the values in the array are used to sort the
observations. If a string or a list of strings, these are interpreted
as column name(s) which are then used to lexicographically sort the
data.
use_distance : bool, default False
Flag indicating whether data should be ordered by the Mahalanobis
distance to the center.
center : {float, int}, default None
If a float, the value must be in [0, 1] and the center is center *
nobs of the ordered data. If an integer, must be in [0, nobs) and
is interpreted as the observation of the ordered data to use.
Returns
-------
fstat : float
The test statistic based on the F test.
pvalue : float
The pvalue of the test.
Notes
-----
This test assumes residuals are homoskedastic and may reject a correct
linear specification if the residuals are heteroskedastic.
"""
if not isinstance(res, RegressionResultsWrapper):
raise TypeError("res must be a results instance from a linear model.")
frac = float_like(frac, "frac")
use_distance = bool_like(use_distance, "use_distance")
nobs = res.nobs
endog = res.model.endog
exog = res.model.exog
if order_by is not None and use_distance:
raise ValueError("order_by and use_distance cannot be simultaneously"
"used.")
if order_by is not None:
if isinstance(order_by, np.ndarray):
order_by = array_like(order_by, "order_by", ndim=1, dtype="int")
else:
if isinstance(order_by, str):
order_by = [order_by]
try:
cols = res.model.data.orig_exog[order_by].copy()
except (IndexError, KeyError):
raise TypeError("order_by must contain valid column names "
"from the exog data used to construct res,"
"and exog must be a pandas DataFrame.")
name = "__index__"
while name in cols:
name += '_'
cols[name] = np.arange(cols.shape[0])
cols = cols.sort_values(order_by)
order_by = np.asarray(cols[name])
endog = endog[order_by]
exog = exog[order_by]
if use_distance:
center = int(nobs) // 2 if center is None else center
if isinstance(center, float):
if not 0.0 <= center <= 1.0:
raise ValueError("center must be in (0, 1) when a float.")
center = int(center * (nobs-1))
else:
center = int_like(center, "center")
if not 0 < center < nobs - 1:
raise ValueError("center must be in [0, nobs) when an int.")
center_obs = exog[center:center+1]
from scipy.spatial.distance import cdist
try:
err = exog - center_obs
vi = np.linalg.inv(err.T @ err / nobs)
except np.linalg.LinAlgError:
err = exog - exog.mean(0)
vi = np.linalg.inv(err.T @ err / nobs)
dist = cdist(exog, center_obs, metric='mahalanobis', VI=vi)
idx = np.argsort(dist.ravel())
endog = endog[idx]
exog = exog[idx]
lowidx = np.ceil(0.5 * (1 - frac) * nobs).astype(int)
uppidx = np.floor(lowidx + frac * nobs).astype(int)
if uppidx - lowidx < exog.shape[1]:
raise ValueError("frac is too small to perform test. frac * nobs"
"must be greater than the number of exogenous"
"variables in the model.")
mi_sl = slice(lowidx, uppidx)
res_mi = OLS(endog[mi_sl], exog[mi_sl]).fit()
nobs_mi = res_mi.model.endog.shape[0]
ss_mi = res_mi.ssr
ss = res.ssr
fstat = (ss - ss_mi) / (nobs - nobs_mi) / ss_mi * res_mi.df_resid
pval = stats.f.sf(fstat, nobs - nobs_mi, res_mi.df_resid)
return fstat, pval
[docs]
def linear_lm(resid, exog, func=None):
"""
Lagrange multiplier test for linearity against functional alternative
# TODO: Remove the restriction
limitations: Assumes currently that the first column is integer.
Currently it does not check whether the transformed variables contain NaNs,
for example log of negative number.
Parameters
----------
resid : ndarray
residuals of a regression
exog : ndarray
exogenous variables for which linearity is tested
func : callable, default None
If func is None, then squares are used. func needs to take an array
of exog and return an array of transformed variables.
Returns
-------
lm : float
Lagrange multiplier test statistic
lm_pval : float
p-value of Lagrange multiplier tes
ftest : ContrastResult instance
the results from the F test variant of this test
Notes
-----
Written to match Gretl's linearity test. The test runs an auxiliary
regression of the residuals on the combined original and transformed
regressors. The Null hypothesis is that the linear specification is
correct.
"""
if func is None:
def func(x):
return np.power(x, 2)
exog = np.asarray(exog)
exog_aux = np.column_stack((exog, func(exog[:, 1:])))
nobs, k_vars = exog.shape
ls = OLS(resid, exog_aux).fit()
ftest = ls.f_test(np.eye(k_vars - 1, k_vars * 2 - 1, k_vars))
lm = nobs * ls.rsquared
lm_pval = stats.chi2.sf(lm, k_vars - 1)
return lm, lm_pval, ftest
[docs]
def spec_white(resid, exog):
"""
White's Two-Moment Specification Test
Parameters
----------
resid : array_like
OLS residuals.
exog : array_like
OLS design matrix.
Returns
-------
stat : float
The test statistic.
pval : float
A chi-square p-value for test statistic.
dof : int
The degrees of freedom.
See Also
--------
het_white
White's test for heteroskedasticity.
Notes
-----
Implements the two-moment specification test described by White's
Theorem 2 (1980, p. 823) which compares the standard OLS covariance
estimator with White's heteroscedasticity-consistent estimator. The
test statistic is shown to be chi-square distributed.
Null hypothesis is homoscedastic and correctly specified.
Assumes the OLS design matrix contains an intercept term and at least
one variable. The intercept is removed to calculate the test statistic.
Interaction terms (squares and crosses of OLS regressors) are added to
the design matrix to calculate the test statistic.
Degrees-of-freedom (full rank) = nvar + nvar * (nvar + 1) / 2
Linearly dependent columns are removed to avoid singular matrix error.
References
----------
.. [*] White, H. (1980). A heteroskedasticity-consistent covariance matrix
estimator and a direct test for heteroscedasticity. Econometrica, 48:
817-838.
"""
x = array_like(exog, "exog", ndim=2)
e = array_like(resid, "resid", ndim=1)
if x.shape[1] < 2 or not np.any(np.ptp(x, 0) == 0.0):
raise ValueError("White's specification test requires at least two"
"columns where one is a constant.")
# add interaction terms
i0, i1 = np.triu_indices(x.shape[1])
exog = np.delete(x[:, i0] * x[:, i1], 0, 1)
# collinearity check - see _fit_collinear
atol = 1e-14
rtol = 1e-13
tol = atol + rtol * exog.var(0)
r = np.linalg.qr(exog, mode="r")
mask = np.abs(r.diagonal()) < np.sqrt(tol)
exog = exog[:, np.where(~mask)[0]]
# calculate test statistic
sqe = e * e
sqmndevs = sqe - np.mean(sqe)
d = np.dot(exog.T, sqmndevs)
devx = exog - np.mean(exog, axis=0)
devx *= sqmndevs[:, None]
b = devx.T.dot(devx)
stat = d.dot(np.linalg.solve(b, d))
# chi-square test
dof = devx.shape[1]
pval = stats.chi2.sf(stat, dof)
return stat, pval, dof
[docs]
@deprecate_kwarg("olsresults", "res")
def recursive_olsresiduals(res, skip=None, lamda=0.0, alpha=0.95,
order_by=None):
"""
Calculate recursive ols with residuals and Cusum test statistic
Parameters
----------
res : RegressionResults
Results from estimation of a regression model.
skip : int, default None
The number of observations to use for initial OLS, if None then skip is
set equal to the number of regressors (columns in exog).
lamda : float, default 0.0
The weight for Ridge correction to initial (X'X)^{-1}.
alpha : {0.90, 0.95, 0.99}, default 0.95
Confidence level of test, currently only two values supported,
used for confidence interval in cusum graph.
order_by : array_like, default None
Integer array specifying the order of the residuals. If not provided,
the order of the residuals is not changed. If provided, must have
the same number of observations as the endogenous variable.
Returns
-------
rresid : ndarray
The recursive ols residuals.
rparams : ndarray
The recursive ols parameter estimates.
rypred : ndarray
The recursive prediction of endogenous variable.
rresid_standardized : ndarray
The recursive residuals standardized so that N(0,sigma2) distributed,
where sigma2 is the error variance.
rresid_scaled : ndarray
The recursive residuals normalize so that N(0,1) distributed.
rcusum : ndarray
The cumulative residuals for cusum test.
rcusumci : ndarray
The confidence interval for cusum test using a size of alpha.
Notes
-----
It produces same recursive residuals as other version. This version updates
the inverse of the X'X matrix and does not require matrix inversion during
updating. looks efficient but no timing
Confidence interval in Greene and Brown, Durbin and Evans is the same as
in Ploberger after a little bit of algebra.
References
----------
jplv to check formulas, follows Harvey
BigJudge 5.5.2b for formula for inverse(X'X) updating
Greene section 7.5.2
Brown, R. L., J. Durbin, and J. M. Evans. “Techniques for Testing the
Constancy of Regression Relationships over Time.”
Journal of the Royal Statistical Society. Series B (Methodological) 37,
no. 2 (1975): 149-192.
"""
if not isinstance(res, RegressionResultsWrapper):
raise TypeError("res a regression results instance")
y = res.model.endog
x = res.model.exog
order_by = array_like(order_by, "order_by", dtype="int", optional=True,
ndim=1, shape=(y.shape[0],))
# intialize with skip observations
if order_by is not None:
x = x[order_by]
y = y[order_by]
nobs, nvars = x.shape
if skip is None:
skip = nvars
rparams = np.nan * np.zeros((nobs, nvars))
rresid = np.nan * np.zeros(nobs)
rypred = np.nan * np.zeros(nobs)
rvarraw = np.nan * np.zeros(nobs)
x0 = x[:skip]
if np.linalg.matrix_rank(x0) < x0.shape[1]:
err_msg = """\
"The initial regressor matrix, x[:skip], issingular. You must use a value of
skip large enough to ensure that the first OLS estimator is well-defined.
"""
raise ValueError(err_msg)
y0 = y[:skip]
# add Ridge to start (not in jplv)
xtxi = np.linalg.inv(np.dot(x0.T, x0) + lamda * np.eye(nvars))
xty = np.dot(x0.T, y0) # xi * y #np.dot(xi, y)
beta = np.dot(xtxi, xty)
rparams[skip - 1] = beta
yipred = np.dot(x[skip - 1], beta)
rypred[skip - 1] = yipred
rresid[skip - 1] = y[skip - 1] - yipred
rvarraw[skip - 1] = 1 + np.dot(x[skip - 1], np.dot(xtxi, x[skip - 1]))
for i in range(skip, nobs):
xi = x[i:i + 1, :]
yi = y[i]
# get prediction error with previous beta
yipred = np.dot(xi, beta)
rypred[i] = np.squeeze(yipred)
residi = yi - yipred
rresid[i] = np.squeeze(residi)
# update beta and inverse(X'X)
tmp = np.dot(xtxi, xi.T)
ft = 1 + np.dot(xi, tmp)
xtxi = xtxi - np.dot(tmp, tmp.T) / ft # BigJudge equ 5.5.15
beta = beta + (tmp * residi / ft).ravel() # BigJudge equ 5.5.14
rparams[i] = beta
rvarraw[i] = np.squeeze(ft)
rresid_scaled = rresid / np.sqrt(rvarraw) # N(0,sigma2) distributed
nrr = nobs - skip
# sigma2 = rresid_scaled[skip-1:].var(ddof=1) #var or sum of squares ?
# Greene has var, jplv and Ploberger have sum of squares (Ass.:mean=0)
# Gretl uses: by reverse engineering matching their numbers
sigma2 = rresid_scaled[skip:].var(ddof=1)
rresid_standardized = rresid_scaled / np.sqrt(sigma2) # N(0,1) distributed
rcusum = rresid_standardized[skip - 1:].cumsum()
# confidence interval points in Greene p136 looks strange. Cleared up
# this assumes sum of independent standard normal, which does not take into
# account that we make many tests at the same time
if alpha == 0.90:
a = 0.850
elif alpha == 0.95:
a = 0.948
elif alpha == 0.99:
a = 1.143
else:
raise ValueError("alpha can only be 0.9, 0.95 or 0.99")
# following taken from Ploberger,
# crit = a * np.sqrt(nrr)
rcusumci = (a * np.sqrt(nrr) + 2 * a * np.arange(0, nobs - skip) / np.sqrt(
nrr)) * np.array([[-1.], [+1.]])
return (rresid, rparams, rypred, rresid_standardized, rresid_scaled,
rcusum, rcusumci)
[docs]
def breaks_hansen(olsresults):
"""
Test for model stability, breaks in parameters for ols, Hansen 1992
Parameters
----------
olsresults : RegressionResults
Results from estimation of a regression model.
Returns
-------
teststat : float
Hansen's test statistic.
crit : ndarray
The critical values at alpha=0.95 for different nvars.
Notes
-----
looks good in example, maybe not very powerful for small changes in
parameters
According to Greene, distribution of test statistics depends on nvar but
not on nobs.
Test statistic is verified against R:strucchange
References
----------
Greene section 7.5.1, notation follows Greene
"""
x = olsresults.model.exog
resid = array_like(olsresults.resid, "resid", shape=(x.shape[0], 1))
nobs, nvars = x.shape
resid2 = resid ** 2
ft = np.c_[x * resid[:, None], (resid2 - resid2.mean())]
score = ft.cumsum(0)
f = nobs * (ft[:, :, None] * ft[:, None, :]).sum(0)
s = (score[:, :, None] * score[:, None, :]).sum(0)
h = np.trace(np.dot(np.linalg.inv(f), s))
crit95 = np.array([(2, 1.01), (6, 1.9), (15, 3.75), (19, 4.52)],
dtype=[("nobs", int), ("crit", float)])
# TODO: get critical values from Bruce Hansen's 1992 paper
return h, crit95
[docs]
def breaks_cusumolsresid(resid, ddof=0):
"""
Cusum test for parameter stability based on ols residuals.
Parameters
----------
resid : ndarray
An array of residuals from an OLS estimation.
ddof : int
The number of parameters in the OLS estimation, used as degrees
of freedom correction for error variance.
Returns
-------
sup_b : float
The test statistic, maximum of absolute value of scaled cumulative OLS
residuals.
pval : float
Probability of observing the data under the null hypothesis of no
structural change, based on asymptotic distribution which is a Brownian
Bridge
crit: list
The tabulated critical values, for alpha = 1%, 5% and 10%.
Notes
-----
Tested against R:structchange.
Not clear: Assumption 2 in Ploberger, Kramer assumes that exog x have
asymptotically zero mean, x.mean(0) = [1, 0, 0, ..., 0]
Is this really necessary? I do not see how it can affect the test statistic
under the null. It does make a difference under the alternative.
Also, the asymptotic distribution of test statistic depends on this.
From examples it looks like there is little power for standard cusum if
exog (other than constant) have mean zero.
References
----------
Ploberger, Werner, and Walter Kramer. “The Cusum Test with OLS Residuals.”
Econometrica 60, no. 2 (March 1992): 271-285.
"""
resid = np.asarray(resid).ravel()
nobs = len(resid)
nobssigma2 = (resid ** 2).sum()
if ddof > 0:
nobssigma2 = nobssigma2 / (nobs - ddof) * nobs
# b is asymptotically a Brownian Bridge
b = resid.cumsum() / np.sqrt(nobssigma2) # use T*sigma directly
# asymptotically distributed as standard Brownian Bridge
sup_b = np.abs(b).max()
crit = [(1, 1.63), (5, 1.36), (10, 1.22)]
# Note stats.kstwobign.isf(0.1) is distribution of sup.abs of Brownian
# Bridge
# >>> stats.kstwobign.isf([0.01,0.05,0.1])
# array([ 1.62762361, 1.35809864, 1.22384787])
pval = stats.kstwobign.sf(sup_b)
return sup_b, pval, crit
# def breaks_cusum(recolsresid):
# """renormalized cusum test for parameter stability based on recursive
# residuals
#
#
# still incorrect: in PK, the normalization for sigma is by T not T-K
# also the test statistic is asymptotically a Wiener Process, Brownian
# motion
# not Brownian Bridge
# for testing: result reject should be identical as in standard cusum
# version
#
# References
# ----------
# Ploberger, Werner, and Walter Kramer. “The Cusum Test with OLS Residuals.”
# Econometrica 60, no. 2 (March 1992): 271-285.
#
# """
# resid = recolsresid.ravel()
# nobssigma2 = (resid**2).sum()
# #B is asymptotically a Brownian Bridge
# B = resid.cumsum()/np.sqrt(nobssigma2) # use T*sigma directly
# nobs = len(resid)
# denom = 1. + 2. * np.arange(nobs)/(nobs-1.) #not sure about limits
# sup_b = np.abs(B/denom).max()
# #asymptotically distributed as standard Brownian Bridge
# crit = [(1,1.63), (5, 1.36), (10, 1.22)]
# #Note stats.kstwobign.isf(0.1) is distribution of sup.abs of Brownian
# Bridge
# #>>> stats.kstwobign.isf([0.01,0.05,0.1])
# #array([ 1.62762361, 1.35809864, 1.22384787])
# pval = stats.kstwobign.sf(sup_b)
# return sup_b, pval, crit
Last update:
Oct 03, 2024