statsmodels.tsa.statespace.structural.UnobservedComponentsResults

class statsmodels.tsa.statespace.structural.UnobservedComponentsResults(model, params, filter_results, cov_type='opg', **kwargs)[source]

Class to hold results from fitting an unobserved components model.

Parameters

model (UnobservedComponents instance) – The fitted model instance

specification

Dictionary including all attributes from the unobserved components model instance.

Type

dictionary

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.

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)

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()

plot_components([which, alpha, observed, …])

Plot the estimated components of the model.

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])

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.

Attributes

autoregressive

Estimates of unobserved autoregressive component

cycle

Estimates of unobserved cycle component

freq_seasonal

Estimates of unobserved frequency domain seasonal component(s)

level

Estimates of unobserved level component

regression_coefficients

Estimates of unobserved regression coefficients

seasonal

Estimates of unobserved seasonal component

trend

Estimates of of unobserved trend component

use_t