Article ID Journal Published Year Pages File Type
5129715 Statistics & Probability Letters 2017 10 Pages PDF
Abstract

Motivated by the double autoregressive (DAR) model, in this paper, we study a vector double autoregressive model (VDAR). The model is a straightforward extension from univariate case to multivariate case. Sufficient ergodicity conditions are given for the model. Without existence of second moment conditions for observed time series, the quasi maximum likelihood estimator (QMLE) of the parameter in the model is shown to be asymptotically normal, which does not hold for classic vector autoregressive (VAR) model with i.i.d errors. Simulation results confirm that our estimators perform well. A given empirical study implies the proposed model has potential applications in practice.

Related Topics
Physical Sciences and Engineering Mathematics Statistics and Probability
Authors
, , , ,