statsmodels.regression.recursive_ls.RecursiveLSResults

class statsmodels.regression.recursive_ls.RecursiveLSResults(model, params, filter_results, cov_type='opg', **kwargs)[source]

Class to hold results from fitting a recursive least squares model.

Parameters
modelRecursiveLS instance

The fitted model instance

Attributes
specificationdictionary

Dictionary including all attributes from the recursive least squares 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, scale, 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_cusum([alpha, legend_loc, fig, figsize])

Plot the CUSUM statistic and significance bounds.

plot_cusum_squares([alpha, legend_loc, fig, …])

Plot the CUSUM of squares statistic and significance bounds.

plot_diagnostics([variable, lags, fig, …])

Diagnostic plots for standardized residuals of one endogenous variable

plot_recursive_coefficient([variables, …])

Plot the recursively estimated coefficients on a given 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, 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[, df_adjust, …])

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.

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, scale, 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_cusum([alpha, legend_loc, fig, figsize])

Plot the CUSUM statistic and significance bounds.

plot_cusum_squares([alpha, legend_loc, fig, …])

Plot the CUSUM of squares statistic and significance bounds.

plot_diagnostics([variable, lags, fig, …])

Diagnostic plots for standardized residuals of one endogenous variable

plot_recursive_coefficient([variables, …])

Plot the recursively estimated coefficients on a given 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, 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[, df_adjust, …])

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.

Properties

aic

(float) Akaike Information Criterion

aicc

(float) Akaike Information Criterion with small sample correction

bic

(float) Bayes Information Criterion

bse

The standard errors of the parameter estimates.

centered_tss

Centered tss

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.

cusum

Cumulative sum of standardized recursive residuals statistics

cusum_squares

Cumulative sum of squares of standardized recursive residuals statistics

ess

fittedvalues

(array) The predicted values of the model.

hqic

(float) Hannan-Quinn Information Criterion

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.

llf_recursive

(float) Loglikelihood defined by recursive residuals, equivalent to OLS

llf_recursive_obs

(float) Loglikelihood at observation, computed from recursive residuals

loglikelihood_burn

(float) The number of observations during which the likelihood is not evaluated.

mae

(float) Mean absolute error

mse

(float) Mean squared error

mse_model

mse_resid

mse_total

pvalues

(array) The p-values associated with the z-statistics of the coefficients.

recursive_coefficients

Estimates of regression coefficients, recursively estimated

resid

(array) The model residuals.

resid_recursive

Recursive residuals

rsquared

sse

(float) Sum of squared errors

ssr

states

tvalues

Return the t-statistic for a given parameter estimate.

uncentered_tss

uncentered tss

use_t

Flag indicating to use the Student’s distribution in inference.

zvalues

(array) The z-statistics for the coefficients.