statsmodels.tsa.statespace.mlemodel.MLEResults

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

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

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

append(endog[, exog, refit])

Recreate the results object with new data appended to the original data

apply(endog[, exog, refit])

Apply the fitted parameters to new data unrelated to the original data

bic()

(float) Bayes Information Criterion

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.

extend(endog[, exog])

Recreate the results object for new data that extends the original data

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

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.