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
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 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.