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
- 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.
- 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.
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
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 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 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
Akaike Information Criterion using Lutkephol’s definition.
The autoregressive lags included in the model
Returns the frequency of the AR roots.
Bayes Information Criterion
The standard errors of the estimated parameters.
The degrees of freedom consumed by the model.
The remaining degrees of freedom in the residuals.
The in-sample predicted values of the fitted AR model.
Final prediction error using Lütkepohl’s definition.
Hannan-Quinn Information Criterion.
Log-likelihood of model
The number of observations after adjusting for losses due to lags.
The estimated parameters.
The two-tailed p values for the t-stats of the params.
The residuals of the model.
The roots of the AR process.
Return the t-statistic for a given parameter estimate.
Flag indicating to use the Student’s distribution in inference.