Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5096427 | Journal of Econometrics | 2011 | 12 Pages |
Abstract
In generalized autoregressive conditional heteroskedastic (GARCH) models, the standard identifiability assumption that the variance of the iid process is equal to 1 can be replaced by an alternative moment assumption. We show that, for estimating the original specification based on the standard identifiability assumption, efficiency gains can be expected from using a quasi-maximum likelihood (QML) estimator based on a non Gaussian density and a reparameterization based on an alternative identifiability assumption. A test allowing to determine whether a reparameterization is needed, that is, whether the more efficient QMLE is obtained with a non Gaussian density, is proposed.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Christian Francq, Guillaume Lepage, Jean-Michel Zakoïan,