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
See specific model class docstring
predict
([start, end, dynamic])Construct in-sample and out-of-sample prediction.
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
See specific model class docstring
predict
([start, end, dynamic])Construct in-sample and out-of-sample prediction.
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
Akaike Information Criterion using Lutkephol’s definition.
Returns the frequency of the AR roots.
Bayes Information Criterion
The standard errors of the estimated parameters.
The in-sample predicted values of the fitted AR model.
Final prediction error using Lütkepohl’s definition.
Hannan-Quinn Information Criterion.
Log-likelihood of model
The p values associated with the standard errors.
The residuals of the model.
The roots of the AR process.
Return the t-statistic for a given parameter estimate.
Flag indicating to use the Student’s distribution in inference.