statsmodels.tsa.ar_model.AutoRegResults¶
-
class
statsmodels.tsa.ar_model.
AutoRegResults
(model, params, cov_params, normalized_cov_params=None, scale=1.0)[source]¶ Class to hold results from fitting an AutoReg model.
Parameters: model : AutoReg
Reference to the model that is fit.
params : ndarray
The fitted parameters from the AR Model.
cov_params : ndarray
The estimated covariance matrix of the model parameters.
normalized_cov_params : ndarray
The array inv(dot(x.T,x)) where x contains the regressors in the model.
scale : float, optional
An estimate of the scale of the model.
Attributes
ar_lags
The autoregressive lags included in the model df_model
The degrees of freedom consumed by the model. df_resid
The remaining degrees of freedom in the residuals. nobs
The number of observations after adjusting for losses due to lags. params
The estimated parameters. use_t
Flag indicating to use the Student’s distribution in inference. Methods
conf_int
([alpha, cols])Construct confidence interval for the fitted parameters. cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix. diagnostic_summary
()Returns a summary containing standard model diagnostic tests f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. 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_diagnostics
([lags, fig, figsize])Diagnostic plots for standardized residuals plot_predict
([start, end, dynamic, exog, …])Plot in- and out-of-sample predictions predict
([start, end, dynamic, exog, exog_oos])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. scale
()sigma2
()summary
([alpha])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
([lags])ARCH-LM test of residual heteroskedasticity test_normality
()Test for normality of standardized residuals. test_serial_correlation
([lags, model_df])Ljung-Box test for residual serial correlation 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
conf_int
([alpha, cols])Construct confidence interval for the fitted parameters. cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix. diagnostic_summary
()Returns a summary containing standard model diagnostic tests f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. 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_diagnostics
([lags, fig, figsize])Diagnostic plots for standardized residuals plot_predict
([start, end, dynamic, exog, …])Plot in- and out-of-sample predictions predict
([start, end, dynamic, exog, exog_oos])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. scale
()sigma2
()summary
([alpha])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
([lags])ARCH-LM test of residual heteroskedasticity test_normality
()Test for normality of standardized residuals. test_serial_correlation
([lags, model_df])Ljung-Box test for residual serial correlation 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
Akaike Information Criterion using Lutkephol’s definition. ar_lags
The autoregressive lags included in the model arfreq
Returns the frequency of the AR roots. bic
Bayes Information Criterion bse
The standard errors of the estimated parameters. df_model
The degrees of freedom consumed by the model. df_resid
The remaining degrees of freedom in the residuals. fittedvalues
The in-sample predicted values of the fitted AR model. fpe
Final prediction error using Lütkepohl’s definition. hqic
Hannan-Quinn Information Criterion. llf
Log-likelihood of model nobs
The number of observations after adjusting for losses due to lags. params
The estimated parameters. pvalues
The two-tailed p values for the t-stats of the params. resid
The residuals of the model. roots
The roots of the AR process. tvalues
Return the t-statistic for a given parameter estimate. use_t
Flag indicating to use the Student’s distribution in inference.