# 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
modelSARIMAX instance

The fitted model instance

Attributes
specificationdictionary

Dictionary including all attributes from the SARIMAX model instance.

polynomial_ararray

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_maarray

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_ararray

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_maarray

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_trendarray

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_orderslist of int

The orders of each of the polynomials in the model.

param_termslist of str

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

Methods

 (float) Akaike Information Criterion (array) Frequency of the roots of the reduced form autoregressive lag polynomial (array) Autoregressive parameters actually estimated in the model. (array) Roots of the reduced form autoregressive lag polynomial (float) Bayes Information Criterion The standard errors of the parameter estimates. 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. (array) The variance / covariance matrix. (array) The variance / covariance matrix. (array) The variance / covariance matrix. (array) The QMLE variance / covariance matrix. (array) The QMLE variance / covariance matrix. (array) The QMLE variance / covariance matrix. f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis. (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 (float) Hannan-Quinn Information Criterion impulse_responses([steps, impulse, …]) Impulse response function info_criteria(criteria[, method]) Information criteria initialize(model, params, **kwd) Initialize (possibly re-initialize) a Results instance. (float) The value of the log-likelihood function evaluated at params. (float) The value of the log-likelihood function evaluated at params. load(fname) load a pickle, (class method) (float) The number of observations during which the likelihood is not evaluated. (array) Frequency of the roots of the reduced form moving average lag polynomial (array) Moving average parameters actually estimated in the model. (array) Roots of the reduced form moving average lag polynomial See specific model class docstring 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 (array) The p-values associated with the z-statistics of the coefficients. remove data arrays, all nobs arrays from result and model (array) The model residuals. save(fname[, remove_data]) save a pickle of this instance (array) Seasonal autoregressive parameters actually estimated in the model. (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 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 (array) The z-statistics for the coefficients.