statsmodels.tsa.ar_model.ARResults
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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 : array
The fitted parameters from the AR Model.
normalized_cov_params : array
inv(dot(X.T,X)) where X is the lagged values.
scale : float, optional
An estimate of the scale of the model.
Returns: **Attributes** :
aic : float
Akaike Information Criterion using Lutkephol?s definition.
bic : float
Bayes Information Criterion
bse : array
The standard errors of the estimated parameters. If
method
is ?cmle?, then the standard errors that are returned are the OLS standard errors of the coefficients. If themethod
is ?mle? then they are computed using the numerical Hessian.fittedvalues : array
The in-sample predicted values of the fitted AR model. The
k_ar
initial values are computed via the Kalman Filter if the model is fit bymle
.fpe : float
Final prediction error using Lutkepohl?s definition ((n_totobs+k_trend)/(n_totobs-k_ar-k_trend))*sigma
hqic : float
Hannan-Quinn Information Criterion.
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
. SeeAR.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
. Sometimesn
in the docs.params : array
The fitted parameters of the model.
pvalues : array
The p values associated with the standard errors.
resid : array
The residuals of the model. If the model is fit by ?mle? then the pre-sample residuals are calculated using fittedvalues from the Kalman Filter.
roots : array
The roots of the AR process are the solution to (1 - arparams[0]*z - arparams[1]*z**2 -...- arparams[p-1]*z**k_ar) = 0 Stability requires that the roots in modulus lie outside the unit circle.
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.
tvalues : array
The t-values associated with
params
.Methods
aic
()bic
()bse
()conf_int
([alpha, cols, method])Returns the confidence interval of the fitted parameters. cov_params
([r_matrix, column, scale, cov_p, ...])Returns the variance/covariance matrix. f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. fittedvalues
()fpe
()hqic
()initialize
(model, params, **kwd)llf
()load
(fname)load a pickle, (class method) normalized_cov_params
()predict
([start, end, dynamic])Returns in-sample and out-of-sample prediction. pvalues
()remove_data
()remove data arrays, all nobs arrays from result and model resid
()roots
()save
(fname[, remove_data])save a pickle of this instance scale
()sigma2
()t_test
(r_matrix[, cov_p, scale, use_t])Compute a t-test for a each linear hypothesis of the form Rb = q tvalues
()Return the t-statistic for a given parameter estimate. wald_test
(r_matrix[, cov_p, scale, invcov, ...])Compute a Wald-test for a joint linear hypothesis. Attributes
extra_doc
preddoc
use_t
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