Munich Personal RePEc Archive

Unified quasi-maximum likelihood estimation theory for stable and unstable Markov bilinear processes

Aknouche, Abdelhakim (2015): Unified quasi-maximum likelihood estimation theory for stable and unstable Markov bilinear processes. Published in: Nova Publisher Science (2015)

[img]
Preview
PDF
MPRA_paper_69572.pdf

Download (218kB) | Preview

Abstract

A unified quasi-maximum likelihood (QML) estimation theory for stationary and nonstationary simple Markov bilinear (SMBL) models is proposed. Such models may be seen as generalized random coefficient autoregressions (GRCA) in which the innovation and the random coefficient processes are fully correlated. It is shown that the QML estimate (QMLE) for the SMBL model is always asymptotically Gaussian without assuming strict stationarity, meaning that there is no knife edge effect. The asymptotic variance of the QMLE is different in the stationary and nonstationary cases but is consistently estimated using the same estimator. A perhaps surprising result is that in the nonstationary domain, all SMBL parameters are consistently estimated in contrast with unstable GARCH and GRCA models where the QMLE of the conditional variance intercept is inconsistent. As a result, strict stationarity testing for the SMBL is studied. Simulation experiments and a real application to strict stationarity testing for some financial stock returns illustrate the theory in finite samples.

UB_LMU-Logo
MPRA is a RePEc service hosted by
the Munich University Library in Germany.