statsmodels.tsa.statespace.mlemodel.MLEResults

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

Class to hold results from fitting a state space model.

Parameters

modelMLEModel instance

The fitted model instance

paramsarray

Fitted parameters

filter_resultsKalmanFilter 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

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.

paramsarray

The parameters of the model.

scalefloat

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

Methods

aic()	(float) Akaike Information Criterion
bic()	(float) Bayes Information Criterion
bse()	The standard errors of the parameter estimates.
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)
loglikelihood_burn()	(float) The number of observations during which the likelihood is not evaluated.
normalized_cov_params()	See specific model class docstring
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, title, 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
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.