Class to hold results from fitting an DynamicFactor model.
| Parameters: | model : DynamicFactor instance
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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() | |
| 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. 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_approx() | (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. An (nobs x k_endog) array. |
| forecast([steps]) | Out-of-sample forecasts |
| get_forecast([steps]) | Out-of-sample forecasts |
| get_prediction([start, end, dynamic, exog]) | In-sample prediction and out-of-sample forecasting |
| hqic() | (float) Hannan-Quinn Information Criterion |
| impulse_responses([steps, impulse, ...]) | Impulse response function |
| 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 |
| normalized_cov_params() | |
| 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 |
| remove_data() | remove data arrays, all nobs arrays from result and model |
| resid() | (array) The model residuals. An (nobs x k_endog) array. |
| 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 |
| 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. |
Methods
| aic() | (float) Akaike Information Criterion |
| bic() | (float) Bayes Information Criterion |
| bse() | |
| 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. 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_approx() | (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. An (nobs x k_endog) array. |
| forecast([steps]) | Out-of-sample forecasts |
| get_forecast([steps]) | Out-of-sample forecasts |
| get_prediction([start, end, dynamic, exog]) | In-sample prediction and out-of-sample forecasting |
| hqic() | (float) Hannan-Quinn Information Criterion |
| impulse_responses([steps, impulse, ...]) | Impulse response function |
| 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 |
| normalized_cov_params() | |
| 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 |
| remove_data() | remove data arrays, all nobs arrays from result and model |
| resid() | (array) The model residuals. An (nobs x k_endog) array. |
| 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 |
| 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. |
Attributes
| factors | Estimates of unobserved factors |
| use_t | bool(x) -> bool |