{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T11:06:42Z","timestamp":1790334402628,"version":"4.1.0"},"reference-count":50,"publisher":"The Econometric Society","issue":"4","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016732","name":"LG Yeonam Cultural Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016732","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ECTA"],"abstract":"<jats:p>We develop a class of tests for time\u2010series models such as multiple regression with growing dimension, infinite\u2010order autoregression, and nonparametric sieve regression. Examples include the Chow test and general linear restriction tests of growing rank \n            <jats:italic>p<\/jats:italic>. Employing such increasing \n            <jats:italic>p<\/jats:italic> asymptotics, we introduce a new scale correction to conventional test statistics, which accounts for a high\u2010order long\u2010run variance (HLV), which emerges as \n            <jats:italic>p<\/jats:italic> grows with sample size. We also propose a bias correction via a null\u2010imposed bootstrap to alleviate finite\u2010sample bias without sacrificing power unduly. A simulation study shows the importance of robustifying testing procedures against the HLV even when \n            <jats:italic>p<\/jats:italic> is moderate. The tests are illustrated with an application to the oil regressions in Hamilton (2003).\n          <\/jats:p>","DOI":"10.3982\/ecta17918","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T12:02:02Z","timestamp":1690459322000},"page":"1333-1361","source":"Crossref","is-referenced-by-count":8,"title":["Robust Inference on Infinite and Growing Dimensional Time\u2010Series Regression"],"prefix":"10.3982","volume":"91","author":[{"given":"Abhimanyu","family":"Gupta","sequence":"first","affiliation":[{"name":"Department of Economics, University of Essex"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Myung Hwan","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Economics, Seoul National 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