statsmodels.tsa.statespace.structural.UnobservedComponentsResults¶
-
class
statsmodels.tsa.statespace.structural.
UnobservedComponentsResults
(model, params, filter_results, cov_type=None, **kwargs)[source]¶ Class to hold results from fitting an unobserved components model.
Parameters: model : UnobservedComponents 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 unobserved components model instance. 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_components
([which, alpha, observed, …])Plot the estimated components of the model. 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])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_components
([which, alpha, observed, …])Plot the estimated components of the model. 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])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 autoregressive
Estimates of unobserved autoregressive component 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. cycle
Estimates of unobserved cycle component fittedvalues
(array) The predicted values of the model. freq_seasonal
Estimates of unobserved frequency domain seasonal component(s) hqic
(float) Hannan-Quinn Information Criterion level
Estimates of unobserved level component 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. regression_coefficients
Estimates of unobserved regression coefficients resid
(array) The model residuals. seasonal
Estimates of unobserved seasonal component sse
(float) Sum of squared errors states
trend
Estimates of of unobserved trend component 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.