Dr. Abhimanyu Gupta (Dept. of Economics, Univ. of Essex)

05/03/2022

Abstract: We develop a class of tests for time series models such as multiple regression with growing dimension, infinite-order autoregression and nonparametric sieve regression. Examples include the Chow test, Andrews and Ploberger (1994)-type exponential tests, and general linear restriction tests, all of growing rank p. Employing such increasing p asymptotics, we introduce a new scale correction to conventional test statistics. This correction accounts for a high-order long-run variance that emerges as p grows with sample size. We propose a bias correction via a null-imposed bootstrap to alleviate finite sample bias without sacrificing power unduly. A simulation study stresses the importance of robustifying testing procedures against the high-order long-run variance even when p is moderate. The tests are illustrated with an application to the oil regressions in Hamilton (2003).