statsmodels.tsa.statespace.dynamic_factor.DynamicFactorResults¶
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class
statsmodels.tsa.statespace.dynamic_factor.
DynamicFactorResults
(model, params, filter_results, cov_type='opg', **kwargs)[source]¶ Class to hold results from fitting an DynamicFactor model.
Parameters: model : DynamicFactor 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 DynamicFactor model instance. coefficient_matrices_var (array) Array containing autoregressive lag polynomial coefficient matrices, ordered from lowest degree to highest. Methods
aic
()(float) Akaike Information Criterion bic
()(float) Bayes Information Criterion bse
()The standard errors of the parameter estimates. coefficients_of_determination
()Coefficients of determination (\(R^2\)) from regressions of individual estimated factors on endogenous variables. 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_coefficients_of_determination
([…])Plot the coefficients of determination 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, separate_params])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.