statsmodels.tsa.statespace.mlemodel.MLEResults

class statsmodels.tsa.statespace.mlemodel.MLEResults(model, params, results, cov_type=None, cov_kwds=None, **kwargs)

Class to hold results from fitting a state space model

Parameters:

modelMLEModel instance

The fitted model instance

paramsndarray

Fitted parameters

resultsKalmanFilter instance

The underlying state space model and Kalman filter output

cov_typestr, optional

The cov_type keyword governs the method for calculating the covariance matrix of parameter estimates. See MLEModel.fit for a description of available options. Default is None, in which case the cov_type is chosen based on whether or not memory_no_likelihood is set on results.

cov_kwdsdict or None, optional

A dictionary of arguments affecting covariance matrix computation. See MLEModel.fit for details. Default is None.

**kwargs

Additional keyword arguments used to initialize the results object.

Attributes:

modelModel instance

A reference to the model that was fit.

filter_resultsKalmanFilter instance

The underlying state space model and Kalman filter output

nobsfloat

The number of observations used to fit the model.

paramsndarray

The parameters of the model.

scalefloat

This is currently set to 1.0 unless the model uses concentrated filtering.

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])	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, invcov])	Compute the F-test for a joint linear hypothesis
forecast([steps, signal_only])	Out-of-sample forecasts
get_forecast([steps, signal_only])	Out-of-sample forecasts and prediction intervals
get_prediction([start, end, dynamic, ...])	In-sample prediction and out-of-sample forecasting
get_smoothed_decomposition([...])	Decompose smoothed output into contributions from observations
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
news(comparison[, impact_date, ...])	Compute impacts from updated data (news and revisions)
normalized_cov_params()	See specific model class docstring
plot_diagnostics([variable, lags, fig, ...])	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, title, model_name, ...])	Summarize the Model
t_test(r_matrix[, cov_p, use_t])	Compute a t-test for 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[, df_adjust, ...])	Ljung-Box test for no serial correlation of standardized residuals
wald_test(r_matrix[, cov_p, invcov, use_f, ...])	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

See also

MLEModel

statsmodels.tsa.statespace.kalman_filter.FilterResults

statsmodels.tsa.statespace.representation.FrozenRepresentation

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])	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, invcov])	Compute the F-test for a joint linear hypothesis
forecast([steps, signal_only])	Out-of-sample forecasts
get_forecast([steps, signal_only])	Out-of-sample forecasts and prediction intervals
get_prediction([start, end, dynamic, ...])	In-sample prediction and out-of-sample forecasting
get_smoothed_decomposition([...])	Decompose smoothed output into contributions from observations
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
news(comparison[, impact_date, ...])	Compute impacts from updated data (news and revisions)
normalized_cov_params()	See specific model class docstring
plot_diagnostics([variable, lags, fig, ...])	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, title, model_name, ...])	Summarize the Model
t_test(r_matrix[, cov_p, use_t])	Compute a t-test for 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[, df_adjust, ...])	Ljung-Box test for no serial correlation of standardized residuals
wald_test(r_matrix[, cov_p, invcov, use_f, ...])	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
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.
fittedvalues	(array) The predicted values of the model.
hqic	(float) Hannan-Quinn Information Criterion
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.
resid	(array) The model residuals.
sse	(float) Sum of squared errors
states
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