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Title: Asymptotic inference for a sign-double autoregressive (SDAR) model Authors:  Emma Iglesias - University of A Coruna (SPAIN) (Spain) [presenting]
Abstract: An extension of the double autoregressive (DAR) model is proposed: the sign-double autoregressive (SDAR) model, in the spirit of the GJR-GARCH model (also named the sign-ARCH model). Our 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 model.