Home>Econometrica>Issues>Supplemental Materials>Consistency and Asymptotic Normality of the Quasi-Maximum Likelihood Estimator in IGARCH(1,1) and Covariance Stationary GARCH(1,1) Models
This paper provides a proof of the consistency and asymptotic normality of the quasi-maximum likelihood estimator in GARCH(1,1) and IGARCH(1,1) models. In contrast to the case of a unit root in the conditional mean, the presence of a "unit root" in the conditional variance does not affect the limiting distribution of the estimators; in both models, estimators are normally distributed. In addition, a consistent estimator of the covariance matrix is available, enabling the use of standard test statistics for inference.
MLA
Lumsdaine, Robin L.. “Consistency and Asymptotic Normality of the Quasi-Maximum Likelihood Estimator in IGARCH(1,1) and Covariance Stationary GARCH(1,1) Models.” Econometrica, vol. 64, .no 3, Econometric Society, 1996, pp. 575-596, https://www.jstor.org/stable/2171862
Chicago
Lumsdaine, Robin L.. “Consistency and Asymptotic Normality of the Quasi-Maximum Likelihood Estimator in IGARCH(1,1) and Covariance Stationary GARCH(1,1) Models.” Econometrica, 64, .no 3, (Econometric Society: 1996), 575-596. https://www.jstor.org/stable/2171862
APA
Lumsdaine, R. L. (1996). Consistency and Asymptotic Normality of the Quasi-Maximum Likelihood Estimator in IGARCH(1,1) and Covariance Stationary GARCH(1,1) Models. Econometrica, 64(3), 575-596. https://www.jstor.org/stable/2171862
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