We give sufficient conditions for strong consistency of estimators for the order of general nonstationary autoregressive models based on the minimization of an information criterion a la Akaike's (1969) AIC. The case of a time-dependent error variance is also covered by the analysis. Furthermore, the more general case of regressor selection in stochastic regression models is treated.
Publié le : 1989-09-14
Classification:
Model selection,
order estimation,
selection of regressors,
strong consistency,
autoregression,
nonstationarity,
nonergodic models,
information criteria,
62M10,
62J05,
60G10,
62F12,
93E12
@article{1176347267,
author = {Potscher, B. M.},
title = {Model Selection Under Nonstationarity: Autoregressive Models and Stochastic Linear Regression Models},
journal = {Ann. Statist.},
volume = {17},
number = {1},
year = {1989},
pages = { 1257-1274},
language = {en},
url = {http://dml.mathdoc.fr/item/1176347267}
}
Potscher, B. M. Model Selection Under Nonstationarity: Autoregressive Models and Stochastic Linear Regression Models. Ann. Statist., Tome 17 (1989) no. 1, pp. 1257-1274. http://gdmltest.u-ga.fr/item/1176347267/