statsmodels.tsa.statespace.structural.UnobservedComponentsResults

class statsmodels.tsa.statespace.structural.UnobservedComponentsResults(model, params, filter_results, cov_type=None, **kwargs)[source]

Class to hold results from fitting an unobserved components model.

Parameters
modelUnobservedComponents instance

The fitted model instance

Attributes
specificationdictionary

Dictionary including all attributes from the unobserved components model instance.

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, cols])

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])

Out-of-sample forecasts

get_forecast([steps])

Out-of-sample forecasts and prediction intervals

get_prediction([start, end, dynamic, index, …])

In-sample prediction and out-of-sample forecasting

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

Plot the estimated components of the model.

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])

Summarize the Model

t_test(r_matrix[, cov_p, 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[, 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.

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, cols])

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])

Out-of-sample forecasts

get_forecast([steps])

Out-of-sample forecasts and prediction intervals

get_prediction([start, end, dynamic, index, …])

In-sample prediction and out-of-sample forecasting

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

Plot the estimated components of the model.

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])

Summarize the Model

t_test(r_matrix[, cov_p, 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[, 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

autoregressive

Estimates of unobserved autoregressive component

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.

cycle

Estimates of unobserved cycle component

fittedvalues

(array) The predicted values of the model.

freq_seasonal

Estimates of unobserved frequency domain seasonal component(s)

hqic

(float) Hannan-Quinn Information Criterion

level

Estimates of unobserved level component

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.

regression_coefficients

Estimates of unobserved regression coefficients

resid

(array) The model residuals.

seasonal

Estimates of unobserved seasonal component

sse

(float) Sum of squared errors

states

trend

Estimates of of unobserved trend component

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.