statsmodels.tsa.ar_model.ARResults

class statsmodels.tsa.ar_model.ARResults(model, params, normalized_cov_params=None, scale=1.0)

Class to hold results from fitting an AR model.

Parameters

modelAR Model instance

Reference to the model that is fit.

paramsndarray

The fitted parameters from the AR Model.

normalized_cov_paramsndarray

The array inv(dot(x.T,x)) where x contains the regressors in the model.

scalefloat, optional

An estimate of the scale of the model.

Attributes

k_arfloat

Lag length. Sometimes used as p in the docs.

k_trendfloat

The number of trend terms included. ‘nc’=0, ‘c’=1.

llffloat

The loglikelihood of the model evaluated at params. See AR.loglike

modelAR model instance

A reference to the fitted AR model.

nobsfloat

The number of available observations nobs - k_ar

n_totobsfloat

The number of total observations in endog. Sometimes n in the docs.

paramsndarray

The fitted parameters of the model.

scalefloat

Same as sigma2

sigma2float

The variance of the innovations (residuals).

trendorderint

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.
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.

scale
sigma2

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 Lutkepohl’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 using Lutkepohl’s definition.
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.