Gupta, Abhimanyu and Seo, Myung Hwan (2023) Robust inference on infinite and growing dimensional time series regression. Econometrica, 91 (4). pp. 1333-1361. DOI https://doi.org/10.3982/ECTA17918
Gupta, Abhimanyu and Seo, Myung Hwan (2023) Robust inference on infinite and growing dimensional time series regression. Econometrica, 91 (4). pp. 1333-1361. DOI https://doi.org/10.3982/ECTA17918
Gupta, Abhimanyu and Seo, Myung Hwan (2023) Robust inference on infinite and growing dimensional time series regression. Econometrica, 91 (4). pp. 1333-1361. DOI https://doi.org/10.3982/ECTA17918
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 and general linear restriction tests of growing rank p. Employing such increasing p asymptotics, we introduce a new scale correction to conventional test statistics which accounts for a high- order long-run variance (HLV) that emerges as p grows with sample size. We also propose a bias correction via a null-imposed bootstrap to alleviate finite sample bias without sacrificing power unduly. A simulation study shows the importance of robustifying testing procedures against the HLV even when p is moderate. The tests are illustrated with an application to the oil regressions in Hamilton (2003).
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Growing number of restrictions; High-order Long-run Variance (HLV); Nonparametric regression; Infinite-order autoregression |
Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Economics, Department of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 05 Mar 2023 19:35 |
Last Modified: | 03 Aug 2023 08:49 |
URI: | http://repository.essex.ac.uk/id/eprint/35072 |
Available files
Filename: Econometrica - 2023 - Gupta.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0