Econometrica

Journal Of The Econometric Society

An International Society for the Advancement of Economic
Theory in its Relation to Statistics and Mathematics

Edited by: Guido W. Imbens • Print ISSN: 0012-9682 • Online ISSN: 1468-0262

Econometrica: Jul, 2023, Volume 91, Issue 4

Robust Inference on Infinite and Growing Dimensional Time-Series Regression

https://doi.org/10.3982/ECTA17918
p. 1333-1361

Abhimanyu Gupta, Myung Hwan Seo

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), which 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).


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Supplemental Material

Supplement to "Robust Inference on Infinite and Growing Dimensional Time Series Regression"

Abhimanyu Gupta and Myung Hwan Seo

This appendix contains material not found within the manuscript.

Supplement to "Robust Inference on Infinite and Growing Dimensional Time Series Regression"

Abhimanyu Gupta and Myung Hwan Seo

This zip file contains the replication files for the manuscript.

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