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:

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, invcov])	Compute the F-test for a joint linear hypothesis
forecast([steps, signal_only])	Out-of-sample forecasts
get_forecast([steps, signal_only])	Out-of-sample forecasts and prediction intervals
get_prediction([start, end, dynamic, ...])	In-sample prediction and out-of-sample forecasting
get_smoothed_decomposition([...])	Decompose smoothed output into contributions from observations
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, use_t])	Compute a t-test for 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

See also

statsmodels.tsa.statespace.kalman_filter.FilterResults

statsmodels.tsa.statespace.mlemodel.MLEResults

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, signal_only])	Out-of-sample forecasts
get_forecast([steps, signal_only])	Out-of-sample forecasts and prediction intervals
get_prediction([start, end, dynamic, ...])	In-sample prediction and out-of-sample forecasting
get_smoothed_decomposition([...])	Decompose smoothed output into contributions from observations
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, use_t])	Compute a t-test for 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
bic	(float) Bayes Information Criterion
bse	The standard errors of the parameter estimates
centered_tss	Centered total sum of squares
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	Explained sum of squares
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	Loglikelihood defined by recursive residuals, equivalent to OLS
llf_recursive_obs	Loglikelihood at each 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	Mean squared error of the model
mse_resid	Mean squared error of the residuals
mse_total	Total mean squared error
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	R-squared
sse	(float) Sum of squared errors
ssr	Sum of squared recursive residuals
states
tvalues	Return the t-statistic for a given parameter estimate
uncentered_tss	Uncentered total sum of squares
use_t	Flag indicating to use the Student's distribution in inference
zvalues	(array) The z-statistics for the coefficients