statsmodels.tsa.ar_model.AutoRegResults¶

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

Class to hold results from fitting an AutoReg model.

Parameters:¶

model : AutoReg¶

Reference to the model that is fit.

params : ndarray¶

The fitted parameters from the AR Model.

cov_params : ndarray¶

The estimated covariance matrix of the model parameters.

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.

use_t : bool, optional¶

Whether use_t was set in fit

summary_text : str, optional¶

Additional text to append to results summary

Methods

append(endog[, exog, refit, fit_kwargs])	Append observations to the ones used to fit the model
apply(endog[, exog, refit, fit_kwargs])	Apply the fitted parameters to new data unrelated to the original data
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, invcov])	Compute the F-test for a joint linear hypothesis.
forecast([steps, exog])	Out-of-sample forecasts
get_prediction([start, end, dynamic, exog, ...])	Predictions and prediction intervals
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, 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, invcov, use_f, ...])	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.
aicc	Akaike Information Criterion with small sample correction
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 using Lutkepohl's definition.
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.