statsmodels.regression.recursive_ls.RecursiveLSResults

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

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

Parameters:

model : RecursiveLS instance

The fitted model instance

See also

statsmodels.tsa.statespace.kalman_filter.FilterResults, statsmodels.tsa.statespace.mlemodel.MLEResults

Attributes

specification

(dictionary) 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.

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
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.