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
- 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.
- k_ar
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
See specific model class docstring
predict
([start, end, dynamic])Construct in-sample and out-of-sample prediction.
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
Akaike Information Criterion using Lutkephol’s definition.
Returns the frequency of the AR roots.
Bayes Information Criterion
The standard errors of the estimated parameters.
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 p values associated with the standard errors.
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.