statsmodels.tsa.statespace.sarimax.SARIMAXResults

class statsmodels.tsa.statespace.sarimax.SARIMAXResults(model, params, filter_results, cov_type='opg', **kwargs)

Class to hold results from fitting an SARIMAX model.

Parameters:

model : SARIMAX instance

The fitted model instance

See also

statsmodels.tsa.statespace.kalman_filter.FilterResults, statsmodels.tsa.statespace.mlemodel.MLEResults

Attributes

specification	(dictionary) Dictionary including all attributes from the SARIMAX model instance.
polynomial_ar	(array) Array containing autoregressive lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).
polynomial_ma	(array) Array containing moving average lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).
polynomial_seasonal_ar	(array) Array containing seasonal autoregressive lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).
polynomial_seasonal_ma	(array) Array containing seasonal moving average lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).
polynomial_trend	(array) Array containing trend polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero).
model_orders	(list of int) The orders of each of the polynomials in the model.
param_terms	(list of str) List of parameters actually included in the model, in sorted order.

Methods

aic()	(float) Akaike Information Criterion
arfreq()	(array) Frequency of the roots of the reduced form autoregressive
arparams()	(array) Autoregressive parameters actually estimated in the model.
arroots()	(array) Roots of the reduced form autoregressive lag polynomial
bic()	(float) Bayes Information Criterion
bse()
conf_int([alpha, cols, method])	Returns the confidence interval of the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, ...])	Returns the variance/covariance matrix.
cov_params_approx()	(array) The variance / covariance matrix. Computed using the numerical
cov_params_oim()	(array) The variance / covariance matrix. Computed using the method
cov_params_opg()	(array) The variance / covariance matrix. Computed using the outer
cov_params_robust()	(array) The QMLE variance / covariance matrix. Alias for
cov_params_robust_approx()	(array) The QMLE variance / covariance matrix. Computed using the
cov_params_robust_oim()	(array) The QMLE variance / covariance matrix. Computed using the
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
fittedvalues()	(array) The predicted values of the model. An (nobs x k_endog) array.
forecast([steps])	Out-of-sample forecasts
get_forecast([steps])	Out-of-sample forecasts
get_prediction([start, end, dynamic, exog])	In-sample prediction and out-of-sample forecasting
hqic()	(float) Hannan-Quinn Information Criterion
impulse_responses([steps, impulse, ...])	Impulse response function
initialize(model, params, **kwd)
llf()	(float) The value of the log-likelihood function evaluated at params.
llf_obs()	(float) The value of the log-likelihood function evaluated at params.
load(fname)	load a pickle, (class method)
loglikelihood_burn()	(float) The number of observations during which the likelihood is not
mafreq()	(array) Frequency of the roots of the reduced form moving average
maparams()	(array) Moving average parameters actually estimated in the model.
maroots()	(array) Roots of the reduced form moving average lag polynomial
normalized_cov_params()
plot_diagnostics([variable, lags, fig, figsize])	Diagnostic plots for standardized residuals of one endogenous variable
predict([start, end, dynamic])	In-sample prediction and out-of-sample forecasting
pvalues()	(array) The p-values associated with the z-statistics of the
remove_data()	remove data arrays, all nobs arrays from result and model
resid()	(array) The model residuals. An (nobs x k_endog) array.
save(fname[, remove_data])	save a pickle of this instance
simulate(nsimulations[, measurement_shocks, ...])	Simulate a new time series following the state space model
summary([alpha, start])	Summarize the Model
t_test(r_matrix[, cov_p, scale, use_t])	Compute a t-test for a each linear hypothesis of the form Rb = q
test_heteroskedasticity(method[, ...])	Test for heteroskedasticity of standardized residuals
test_normality(method)	Test for normality of standardized residuals.
test_serial_correlation(method[, lags])	Ljung-box test for no serial correlation of standardized residuals
tvalues()	Return the t-statistic for a given parameter estimate.
wald_test(r_matrix[, cov_p, scale, invcov, ...])	Compute a Wald-test for a joint linear hypothesis.
wald_test_terms([skip_single, ...])	Compute a sequence of Wald tests for terms over multiple columns
zvalues()	(array) The z-statistics for the coefficients.

Methods

aic()	(float) Akaike Information Criterion
arfreq()	(array) Frequency of the roots of the reduced form autoregressive
arparams()	(array) Autoregressive parameters actually estimated in the model.
arroots()	(array) Roots of the reduced form autoregressive lag polynomial
bic()	(float) Bayes Information Criterion
bse()
conf_int([alpha, cols, method])	Returns the confidence interval of the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, ...])	Returns the variance/covariance matrix.
cov_params_approx()	(array) The variance / covariance matrix. Computed using the numerical
cov_params_oim()	(array) The variance / covariance matrix. Computed using the method
cov_params_opg()	(array) The variance / covariance matrix. Computed using the outer
cov_params_robust()	(array) The QMLE variance / covariance matrix. Alias for
cov_params_robust_approx()	(array) The QMLE variance / covariance matrix. Computed using the
cov_params_robust_oim()	(array) The QMLE variance / covariance matrix. Computed using the
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
fittedvalues()	(array) The predicted values of the model. An (nobs x k_endog) array.
forecast([steps])	Out-of-sample forecasts
get_forecast([steps])	Out-of-sample forecasts
get_prediction([start, end, dynamic, exog])	In-sample prediction and out-of-sample forecasting
hqic()	(float) Hannan-Quinn Information Criterion
impulse_responses([steps, impulse, ...])	Impulse response function
initialize(model, params, **kwd)
llf()	(float) The value of the log-likelihood function evaluated at params.
llf_obs()	(float) The value of the log-likelihood function evaluated at params.
load(fname)	load a pickle, (class method)
loglikelihood_burn()	(float) The number of observations during which the likelihood is not
mafreq()	(array) Frequency of the roots of the reduced form moving average
maparams()	(array) Moving average parameters actually estimated in the model.
maroots()	(array) Roots of the reduced form moving average lag polynomial
normalized_cov_params()
plot_diagnostics([variable, lags, fig, figsize])	Diagnostic plots for standardized residuals of one endogenous variable
predict([start, end, dynamic])	In-sample prediction and out-of-sample forecasting
pvalues()	(array) The p-values associated with the z-statistics of the
remove_data()	remove data arrays, all nobs arrays from result and model
resid()	(array) The model residuals. An (nobs x k_endog) array.
save(fname[, remove_data])	save a pickle of this instance
simulate(nsimulations[, measurement_shocks, ...])	Simulate a new time series following the state space model
summary([alpha, start])	Summarize the Model
t_test(r_matrix[, cov_p, scale, use_t])	Compute a t-test for a each linear hypothesis of the form Rb = q
test_heteroskedasticity(method[, ...])	Test for heteroskedasticity of standardized residuals
test_normality(method)	Test for normality of standardized residuals.
test_serial_correlation(method[, lags])	Ljung-box test for no serial correlation of standardized residuals
tvalues()	Return the t-statistic for a given parameter estimate.
wald_test(r_matrix[, cov_p, scale, invcov, ...])	Compute a Wald-test for a joint linear hypothesis.
wald_test_terms([skip_single, ...])	Compute a sequence of Wald tests for terms over multiple columns
zvalues()	(array) The z-statistics for the coefficients.

Attributes

use_t