Robust Inference on Infinite and Growing Dimensional Time-Series Regression

• 成果类型: Article
• 署名作者: Gupta, Abhimanyu; Seo, Myung Hwan
• 署名单位: University of Essex; Seoul National University (SNU)
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA17918
• 发表日期: 2023
• 页码: 1333-1361
• 关键词: Asymptotic Normality; conditional heteroskedasticity; linear-regression; convergence-rates; autocorrelation; estimators; models; ORDER; selection; tests

摘要

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).