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.