statsmodels.tsa.statespace.mlemodel.MLEResults¶
-
class
statsmodels.tsa.statespace.mlemodel.
MLEResults
(model, params, filter_results, cov_type='opg', cov_kwds=None, **kwargs)[source]¶ Class to hold results from fitting a state space model.
Parameters: model : MLEModel instance
The fitted model instance
params : array
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 array The parameters of the model. scale float This is currently set to 1.0 and not used by the model or its results. Methods
aic
()(float) Akaike Information Criterion bic
()(float) Bayes Information Criterion bse
()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_cs
()(array) The variance / covariance matrix. Computed using the numerical cov_params_delta
()(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_cs
()(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. forecast
([steps])Out-of-sample forecasts hqic
()(float) Hannan-Quinn Information Criterion 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
()predict
([start, end, dynamic, full_results])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. save
(fname[, remove_data])save a pickle of this instance summary
([alpha, start, 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 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
use_t