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Long-run predictability tests are even worse than you thought
Department of Economics and the Centrefor Finance, University of Gothenburg,Gothenburg, Sweden.
Örebro University, Örebro University School of Business.ORCID iD: 0000-0001-8124-328x
2022 (English)In: Journal of applied econometrics (Chichester, England), ISSN 0883-7252, E-ISSN 1099-1255, Vol. 37, no 7, p. 1334-1355Article in journal (Refereed) Published
Abstract [en]

We derive asymptotic results for the long-horizon ordinary least squares (OLS) estimator and corresponding t$$ t $$-statistic for stationary autoregressive predictors. The t$$ t $$-statistic-formed using the correct asymptotic variance-together with standard-normal critical values result in a correctly-sized test for exogenous predictors. For endogenous predictors, the test is size distorted regardless of the persistence in the predictor and adjusted critical values are necessary. The endogeneity problem stems from the long-run estimation and is distinct from the ordinary persistence-dependent "Stambaugh" bias. The bias for fully stationary predictors appears not to have been previously noted and adds further difficulty to inference in long-run predictive regressions.

Place, publisher, year, edition, pages
John Wiley & Sons, 2022. Vol. 37, no 7, p. 1334-1355
Keywords [en]
endogeneity bias, long-run relationships, predictive regressions
National Category
Economics
Identifiers
URN: urn:nbn:se:oru:diva-101724DOI: 10.1002/jae.2930ISI: 000861455500001Scopus ID: 2-s2.0-85138954848OAI: oai:DiVA.org:oru-101724DiVA, id: diva2:1702993
Funder
The Jan Wallander and Tom Hedelius Foundation, W19-0021Available from: 2022-10-12 Created: 2022-10-12 Last updated: 2023-02-03Bibliographically approved

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Kiss, Tamás

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