statsmodels.tsa.statespace.sarimax.SARIMAXResults¶
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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
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_cs
()(array) The variance / covariance matrix. Computed using the numerical cov_params_delta
()(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_cs
()(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. forecast
([steps, exog])Out-of-sample forecasts hqic
()(float) Hannan-Quinn Information Criterion 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
()predict
([start, end, exog, 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. save
(fname[, remove_data])save a pickle of this instance 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 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