statsmodels.tsa.statespace.mlemodel.MLEResults¶
-
class
statsmodels.tsa.statespace.mlemodel.
MLEResults
(model, params, results, cov_type='opg', cov_kwds=None, **kwargs)[source]¶ Class to hold results from fitting a state space model.
Parameters: model : MLEModel instance
The fitted model instance
params : array
Fitted parameters
filter_results : KalmanFilter instance
The underlying state space model and Kalman filter output
See also
MLEModel
,statsmodels.tsa.statespace.kalman_filter.FilterResults
,statsmodels.tsa.statespace.representation.FrozenRepresentation
Attributes
model (Model instance) A reference to the model that was fit. filter_results (KalmanFilter instance) The underlying state space model and Kalman filter output nobs (float) The number of observations used to fit the model. params (array) The parameters of the model. scale (float) This is currently set to 1.0 unless the model uses concentrated filtering. Methods
aic
()(float) Akaike Information Criterion bic
()(float) Bayes Information Criterion bse
()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. 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)Initialize (possibly re-initialize) a Results instance. 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); use only on trusted files, as unpickling can run arbitrary code. loglikelihood_burn
()(float) The number of observations during which the likelihood is not evaluated. normalized_cov_params
()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 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 simulate
(nsimulations[, measurement_shocks, …])Simulate a new time series following the state space model summary
([alpha, start, title, model_name, …])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.