A0153
Title: Asymptotic inference for new double autoregressive models
Authors: Emma Iglesias - University of A Coruna (SPAIN) (Spain) [presenting]
Abstract: Extensions of the double autoregressive (DAR) model are proposed. We start with the novel sign-double autoregressive (SDAR) model, in the spirit of the GJR-GARCH model (also named the sign-ARCH model). The new model shares the important property of DAR models where a unit root does not imply nonstationarity and allows for asymmetry. We establish consistency and asymptotic normality of the quasi-maximum likelihood estimator in the context of the SDAR model. Furthermore, it is shown by simulations that the asymptotic properties also apply in finite samples. Finally, an empirical application shows the usefulness of our new model. New DAR models will also be proposed, and the corresponding asymptotic theory and empirical examples will be provided.
