Colloquium
The Department of Statistics

presents

Yoosoon Chang
Department of Economics
Rice University

Vector Autoregressions with Unknown Mixtures of I(0), I(1) and I(2) Components

Abstract

This paper develops an efficient method of estimation for nonstationary vector autoregressions (VAR's) with unknown mixtures of I(0), I(1) and I(2) components. The method does not require prior knowledge on the exact number and location of unit roots in the system. It is, therefore, applicable for VAR's with any mixture of I(0), I(1) and I(2) variables which may be cointegrated in any form. Our estimator, however, has the same limiting distribution as the maximum likelihood estimator under Gaussian assumptions presuming the precise knowledge of the order of unit roots and cointegrating relationships.

Moreover, the inference based on the estimator yields Wald tests that are asymptotically distributed as a weighted sum of independent chi-square variates with weights between zero and one, and this has a direct application for Granger-causality testing in nonstationary VAR's.}