STATISTICAL INFERENCE FOR MEASUREMENT EQUATION SELECTION IN THE LOG-REALGARCH MODEL. (October 2019)

• Record Type: Journal Article
• Authors: Li, Yu-Ning; Zhang, Yi; Zhang, Caiya
• Is Part Of: Econometric theory. Volume 35:Number 5(2019)
• Page Start: 943
• Page End: 977
• Publication Date: 2019-10
• Subjects: Econometrics -- Periodicals; 330.01519505
• DOI: 10.1017/S0266466618000348
• Languages: English
• ISSNs: 0266-4666

Abstract

Abstract : This article investigates the statistical inference problem of whether a measurement equation is self-consistent in the logarithmic realized GARCH model (log-RealGARCH). First, we provide the sufficient and necessary conditions for the strict stationarity of both the log-RealGARCH model and the log-GARCH-X model. Under these conditions, strong consistency and asymptotic normality of the quasi-maximum likelihood estimators of these two models are obtained. Then, based on the asymptotic results, we propose a Hausman-type self-consistency test for diagnosing the suitability of the measurement equation in the log-RealGARCH model. Finally, the results of simulations and an empirical study are found to accord with the theoretical results.