A0423
Title: Robust inference on infinite and growing dimensional time series regression
Authors: Abhimanyu Gupta - University of Essex (United Kingdom) [presenting]
Myung Hwan Seo - Seoul National University (Korea, South)
Abstract: A class of tests is developed for a growing number of restrictions in infinite and increasing order time series models such as multiple regression with growing dimension, infinite-order autoregression and nonparametric sieve regression. Examples include the Chow test, exponential tests, and testing of general linear restrictions of growing rank $p$. Notably, our tests introduce a new scale correction to the conventional quadratic forms that are recentered and normalized to account for diverging p. 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 control 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 oil regressions.
