Augmented cointegrating linear models with possibly strongly correlated stationary and nonstationary regressors

Abstract

This paper proposes a class of augmented cointegrating linear (ACL) models that accommodate both stationary and nonstationary variables in a single equation, where regressors may be strongly correlated defined below. Moreover, the limit theory of estimators proposed is established via joint convergence of the sample variance and covariance that eschews some existing drawback in the literature; to facilitate inference, a self-normalized central limit theorem is given as well. Furthermore, numerical simulations confirm the theoretical results, and for the US’s GDP series we show that ACL model outperforms some competing models.