statsmodels.tsa.ar_model.ARResults¶
-
class
statsmodels.tsa.ar_model.
ARResults
(model, params, normalized_cov_params=None, scale=1.0)[source]¶ Class to hold results from fitting an AR model.
Parameters: model : AR Model instance
Reference to the model that is fit.
params : ndarray
The fitted parameters from the AR Model.
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
k_ar (float) Lag length. Sometimes used as p in the docs. k_trend (float) The number of trend terms included. ‘nc’=0, ‘c’=1. llf (float) The loglikelihood of the model evaluated at params. See AR.loglike model (AR model instance) A reference to the fitted AR model. nobs (float) The number of available observations nobs - k_ar n_totobs (float) The number of total observations in endog. Sometimes n in the docs. params (ndarray) The fitted parameters of the model. scale (float) Same as sigma2 sigma2 (float) The variance of the innovations (residuals). trendorder (int) The polynomial order of the trend. ‘nc’ = None, ‘c’ or ‘t’ = 0, ‘ct’ = 1, etc. 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. 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 predict
([start, end, dynamic])Construct in-sample and out-of-sample prediction. 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. 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. 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 predict
([start, end, dynamic])Construct in-sample and out-of-sample prediction. 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. 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. arfreq
Returns the frequency of the AR roots. bic
Bayes Information Criterion bse
The standard errors of the estimated parameters. 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 pvalues
The p values associated with the standard errors. 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.