Estimation in nonstationary random coefficient autoregressive models
Methodology
2009-03-03 v1 Statistics Theory
Statistics Theory
Abstract
We investigate the estimation of parameters in the random coefficient autoregressive model. We consider a nonstationary RCA process and show that the innovation variance parameter cannot be estimated by the quasi-maximum likelihood method. The asymptotic normality of the quasi-maximum likelihood estimator for the remaining model parameters is proven so the unit root problem does not exist in the random coefficient autoregressive model.
Cite
@article{arxiv.0903.0022,
title = {Estimation in nonstationary random coefficient autoregressive models},
author = {Istvan Berkes and Lajos Horvath and Shiqing Ling},
journal= {arXiv preprint arXiv:0903.0022},
year = {2009}
}
Comments
21 pages