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:

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_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])	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_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, 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
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()
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])	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_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, 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
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

use_t