statsmodels.tsa.statespace.sarimax.SARIMAXResults

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

Class to hold results from fitting an SARIMAX model.

Parameters:	model (SARIMAX instance) – The fitted model instance

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.

See also

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

Methods

aic()	(float) Akaike Information Criterion
arfreq()	(array) Frequency of the roots of the reduced form autoregressive lag polynomial
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.
cov_params_oim()	(array) The variance / covariance matrix.
cov_params_opg()	(array) The variance / covariance matrix.
cov_params_robust()	(array) The QMLE variance / covariance matrix.
cov_params_robust_approx()	(array) The QMLE variance / covariance matrix.
cov_params_robust_oim()	(array) The QMLE variance / covariance matrix.
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.
forecast([steps])	Out-of-sample forecasts
get_forecast([steps])	Out-of-sample forecasts
get_prediction([start, end, dynamic, index, …])	In-sample prediction and out-of-sample forecasting
hqic()	(float) Hannan-Quinn Information Criterion
impulse_responses([steps, impulse, …])	Impulse response function
info_criteria(criteria[, method])	Information criteria
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 evaluated.
mafreq()	(array) Frequency of the roots of the reduced form moving average lag polynomial
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 coefficients.
remove_data()	remove data arrays, all nobs arrays from result and model
resid()	(array) The model residuals.
save(fname[, remove_data])	save a pickle of this instance
seasonalarparams()	(array) Seasonal autoregressive parameters actually estimated in the model.
seasonalmaparams()	(array) Seasonal moving average parameters actually estimated in the model.
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
t_test_pairwise(term_name[, method, alpha, …])	perform pairwise t_test with multiple testing corrected p-values
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