# 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 including all attributes from the SARIMAX model instance.

Type: dictionary
polynomial_ar

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).

Type: array
polynomial_ma

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).

Type: array
polynomial_seasonal_ar

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).

Type: array
polynomial_seasonal_ma

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).

Type: array
polynomial_trend

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).

Type: array
model_orders

The orders of each of the polynomials in the model.

Type: list of int
param_terms

List of parameters actually included in the model, in sorted order.

Type: list of str

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