statsmodels.tsa.statespace.mlemodel.MLEResults¶
-
class
statsmodels.tsa.statespace.mlemodel.
MLEResults
(model, params, results, cov_type=None, cov_kwds=None, **kwargs)[source]¶ Class to hold results from fitting a state space model.
Parameters: model : MLEModel instance
The fitted model instance
params : ndarray
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 (ndarray) The parameters of the model. scale (float) This is currently set to 1.0 unless the model uses concentrated filtering. Methods
append
(endog[, exog, refit, fit_kwargs])Recreate the results object with new data appended to the original data apply
(endog[, exog, refit, fit_kwargs])Apply the fitted parameters to new data unrelated to the original data conf_int
([alpha, cols])Construct confidence interval for the fitted parameters. cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix. extend
(endog[, exog, fit_kwargs])Recreate the results object for new data that extends the original data f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. 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 impulse_responses
([steps, impulse, …])Impulse response function info_criteria
(criteria[, method])Information criteria initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance. load
(fname)Load a pickled results instance 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 remove_data
()Remove data arrays, all nobs arrays from result and model. 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 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. Methods
append
(endog[, exog, refit, fit_kwargs])Recreate the results object with new data appended to the original data apply
(endog[, exog, refit, fit_kwargs])Apply the fitted parameters to new data unrelated to the original data conf_int
([alpha, cols])Construct confidence interval for the fitted parameters. cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix. extend
(endog[, exog, fit_kwargs])Recreate the results object for new data that extends the original data f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. 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 impulse_responses
([steps, impulse, …])Impulse response function info_criteria
(criteria[, method])Information criteria initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance. load
(fname)Load a pickled results instance 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 remove_data
()Remove data arrays, all nobs arrays from result and model. 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 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. Properties
aic
(float) Akaike Information Criterion aicc
(float) Akaike Information Criterion with small sample correction bic
(float) Bayes Information Criterion bse
The standard errors of the parameter estimates. 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. fittedvalues
(array) The predicted values of the model. hqic
(float) Hannan-Quinn Information Criterion 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. loglikelihood_burn
(float) The number of observations during which the likelihood is not evaluated. mae
(float) Mean absolute error mse
(float) Mean squared error pvalues
(array) The p-values associated with the z-statistics of the coefficients. resid
(array) The model residuals. sse
(float) Sum of squared errors states
tvalues
Return the t-statistic for a given parameter estimate. use_t
Flag indicating to use the Student’s distribution in inference. zvalues
(array) The z-statistics for the coefficients.