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 |