Statistical inference for time-varying ARCH processes

Thumbnail Image

Date

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Description

In this paper the class of ARCH$(\infty)$ models is generalized to the nonstationary class of ARCH$(\infty)$ models with time-varying coefficients. For fixed time points, a stationary approximation is given leading to the notation ``locally stationary ARCH$(\infty)$ process.'' The asymptotic properties of weighted quasi-likelihood estimators of time-varying ARCH$(p)$ processes ($p<\infty$) are studied, including asymptotic normality. In particular, the extra bias due to nonstationarity of the process is investigated. Moreover, a Taylor expansion of the nonstationary ARCH process in terms of stationary processes is given and it is proved that the time-varying ARCH process can be written as a time-varying Volterra series.
Published at http://dx.doi.org/10.1214/009053606000000227 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Keywords

Statistics Theory, 62M10 (Primary) 62F10 (Secondary)

Citation

URI

http://salesiana.dossiersoluciones.com/handle/123456789/131131

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/math/0607799
 http://arxiv.org/abs/math/0607799
 Annals of Statistics 2006, Vol. 34, No. 3, 1075-1114
 doi:10.1214/009053606000000227

Collections

arXiv