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A Bootstrap Stationarity Test for Predictive Regression Invalidity

Georgiev, I and Harvey, DI and Taylor, AMR and Leybourne, SJ (2017) 'A Bootstrap Stationarity Test for Predictive Regression Invalidity.' Journal of Business and Economic Statistics. ISSN 0735-0015

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Abstract

In order for predictive regression tests to deliver asymptotically valid inference, account has to be taken of the degree of persistence of the predictors under test. There is also a maintained assumption that any predictability in the variable of interest is purely attributable to the predictors under test. Violation of this assumption by the omission of relevant persistent predictors renders the predictive regression invalid, and potentially also spurious, as both the finite sample and asymptotic size of the predictability tests can be significantly in ated. In response we propose a predictive regression invalidity test based on a stationarity testing approach. To allow for an unknown degree of persistence in the putative predictors, and for heteroskedasticity in the data, we implement our proposed test using a fixed regressor wild bootstrap procedure. We demonstrate the asymptotic validity of the proposed bootstrap test by proving that the limit distribution of the bootstrap statistic, conditional on the data, is the same as the limit null distribution of the statistic computed on the original data, conditional on the predictor. This corrects a long-standing error in the bootstrap literature whereby it is incorrectly argued that for strongly persistent regressors and test statistics akin to ours the validity of the fixed regressor bootstrap obtains through equivalence to an unconditional limit distribution. Our bootstrap results are therefore of interest in their own right and are likely to have applications beyond the present context. An illustration is given by re-examining the results relating to U.S. stock returns data in Campbell and Yogo (2006).

Item Type: Article
Uncontrolled Keywords: Predictive regression, Granger causality, persistence, stationarity test, fixed regressor wild bootstrap, conditional distribution
Subjects: H Social Sciences > HG Finance
Divisions: Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Elements
Date Deposited: 02 Oct 2017 10:12
Last Modified: 17 May 2018 10:15
URI: http://repository.essex.ac.uk/id/eprint/20445

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