statsmodels.tsa.ar_model.AutoRegResults

class statsmodels.tsa.ar_model.AutoRegResults(model, params, cov_params, normalized_cov_params=None, scale=1.0)[source]

Class to hold results from fitting an AutoReg model.

Parameters
modelAutoReg

Reference to the model that is fit.

paramsndarray

The fitted parameters from the AR Model.

cov_paramsndarray

The estimated covariance matrix of the model parameters.

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.

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.

diagnostic_summary()

Returns a summary containing standard model diagnostic tests

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

plot_diagnostics([lags, fig, figsize])

Diagnostic plots for standardized residuals

plot_predict([start, end, dynamic, exog, …])

Plot in- and out-of-sample predictions

predict([start, end, dynamic, exog, exog_oos])

In-sample prediction and out-of-sample forecasting.

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.

test_heteroskedasticity([lags])

ARCH-LM test of residual heteroskedasticity

test_normality()

Test for normality of standardized residuals.

test_serial_correlation([lags, model_df])

Ljung-Box test for residual serial correlation

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.

ar_lags

The autoregressive lags included in the model

arfreq

Returns the frequency of the AR roots.

bic

Bayes Information Criterion

bse

The standard errors of the estimated parameters.

df_model

The degrees of freedom consumed by the model.

df_resid

The remaining degrees of freedom in the residuals.

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

nobs

The number of observations after adjusting for losses due to lags.

params

The estimated parameters.

pvalues

The two-tailed p values for the t-stats of the params.

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.