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

aic()	(float) Akaike Information Criterion
bic()	(float) Bayes Information Criterion
bse()
conf_int([alpha, cols, method])	Returns the confidence interval of the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, ...])	Returns the variance/covariance matrix.
cov_params_approx()	(array) The variance / covariance matrix. Computed using the numerical
cov_params_oim()	(array) The variance / covariance matrix. Computed using the method
cov_params_opg()	(array) The variance / covariance matrix. Computed using the outer
cov_params_robust()	(array) The QMLE variance / covariance matrix. Alias for
cov_params_robust_approx()	(array) The QMLE variance / covariance matrix. Computed using the
cov_params_robust_oim()	(array) The QMLE variance / covariance matrix. Computed using the
cusum()	Cumulative sum of standardized recursive residuals statistics
cusum_squares()	Cumulative sum of squares of standardized recursive residuals
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
fittedvalues()	(array) The predicted values of the model. An (nobs x k_endog) array.
forecast([steps])	Out-of-sample forecasts
get_forecast([steps])	Out-of-sample forecasts
get_prediction([start, end, dynamic])	In-sample prediction and out-of-sample forecasting
hqic()	(float) Hannan-Quinn Information Criterion
impulse_responses([steps, impulse, ...])	Impulse response function
initialize(model, params, **kwd)
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.
load(fname)	load a pickle, (class method)
loglikelihood_burn()	(float) The number of observations during which the likelihood is not
normalized_cov_params()
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
pvalues()	(array) The p-values associated with the z-statistics of the
remove_data()	remove data arrays, all nobs arrays from result and model
resid()	(array) The model residuals. An (nobs x k_endog) array.
resid_recursive()	Recursive residuals
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
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
tvalues()	Return the t-statistic for a given parameter estimate.
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
zvalues()	(array) The z-statistics for the coefficients.

Methods

aic()	(float) Akaike Information Criterion
bic()	(float) Bayes Information Criterion
bse()
conf_int([alpha, cols, method])	Returns the confidence interval of the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, ...])	Returns the variance/covariance matrix.
cov_params_approx()	(array) The variance / covariance matrix. Computed using the numerical
cov_params_oim()	(array) The variance / covariance matrix. Computed using the method
cov_params_opg()	(array) The variance / covariance matrix. Computed using the outer
cov_params_robust()	(array) The QMLE variance / covariance matrix. Alias for
cov_params_robust_approx()	(array) The QMLE variance / covariance matrix. Computed using the
cov_params_robust_oim()	(array) The QMLE variance / covariance matrix. Computed using the
cusum()	Cumulative sum of standardized recursive residuals statistics
cusum_squares()	Cumulative sum of squares of standardized recursive residuals
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
fittedvalues()	(array) The predicted values of the model. An (nobs x k_endog) array.
forecast([steps])	Out-of-sample forecasts
get_forecast([steps])	Out-of-sample forecasts
get_prediction([start, end, dynamic])	In-sample prediction and out-of-sample forecasting
hqic()	(float) Hannan-Quinn Information Criterion
impulse_responses([steps, impulse, ...])	Impulse response function
initialize(model, params, **kwd)
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.
load(fname)	load a pickle, (class method)
loglikelihood_burn()	(float) The number of observations during which the likelihood is not
normalized_cov_params()
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
pvalues()	(array) The p-values associated with the z-statistics of the
remove_data()	remove data arrays, all nobs arrays from result and model
resid()	(array) The model residuals. An (nobs x k_endog) array.
resid_recursive()	Recursive residuals
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
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
tvalues()	Return the t-statistic for a given parameter estimate.
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
zvalues()	(array) The z-statistics for the coefficients.

Attributes

recursive_coefficients	Estimates of regression coefficients, recursively estimated
use_t