Seminars and Workshops
3:30pm - 5:00pm
Host: Ivan Korolev
"Robust Inference on Infinite and Growing Dimensional Regression" (with Myung Hwan 'Matt' Seo)
Abstract: We develop a class of tests for a growing number of restrictions in infinite and increasing order time series models such as infinite-order autoregression, nonparametric sieve regression and multiple regression with growing dimension. Examples includes the Chow test, Andrews and Ploberger (1994) type 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 captures high-order autocovariances that emerge as p grows with sample size in time series regression. Furthermore, we propose a bias correction by a null-imposed bootstrap to control finite sample bias without sacrificing power unduly. A simulation study stresses the importance of robustifying testing procedures against high-order autocovariances even when p is moderate. The tests are illustrated with an application to the oil regression in Hamilton (2003).
"Robust Inference on Infinite and Growing Dimensional Regression" (with Myung Hwan 'Matt' Seo)
Abstract: We develop a class of tests for a growing number of restrictions in infinite and increasing order time series models such as infinite-order autoregression, nonparametric sieve regression and multiple regression with growing dimension. Examples includes the Chow test, Andrews and Ploberger (1994) type 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 captures high-order autocovariances that emerge as p grows with sample size in time series regression. Furthermore, we propose a bias correction by a null-imposed bootstrap to control finite sample bias without sacrificing power unduly. A simulation study stresses the importance of robustifying testing procedures against high-order autocovariances even when p is moderate. The tests are illustrated with an application to the oil regression in Hamilton (2003).
