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• W2100602094 abstract "This article investigates the asymptotic properties of the Gaussian quasi-maximum-likelihood estimators (QMLE’s) of the GARCH model augmented by including an additional explanatory variable—the so-called GARCH-X model. The additional covariate is allowed to exhibit any degree of persistence as captured by its long-memory parameter dx; in particular, we allow for both stationary and nonstationary covariates. We show that the QMLE’s of the parameters entering the volatility equation are consistent and mixed-normally distributed in large samples. The convergence rates and limiting distributions of the QMLE’s depend on whether the regressor is stationary or not. However, standard inferential tools for the parameters are robust to the level of persistence of the regressor with t-statistics following standard Normal distributions in large sample irrespective of whether the regressor is stationary or not. Supplementary materials for this article are available online." @default.
• W2100602094 created "2016-06-24" @default.
• W2100602094 creator A5028857555 @default.
• W2100602094 creator A5046585599 @default.
• W2100602094 date "2014-07-03" @default.
• W2100602094 modified "2023-09-28" @default.
• W2100602094 title "Asymptotic Theory for the QMLE in GARCH-X Models With Stationary and Nonstationary Covariates" @default.
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• W2100602094 doi "https://doi.org/10.1080/07350015.2014.897954" @default.
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