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

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

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, figsize])

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[, 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.

Properties

aic

(float) Akaike Information Criterion

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

esss

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.

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

ssr

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.