A simple approach for diagnosing instabilities in predictive regressions
A simple approach for diagnosing instabilities in predictive regressions
We introduce a method for detecting the presence of structural breaks in the parameters of predictive regressions linking noisy variables such as stock returns to persistent predictors such as valuation ratios. Our approach relies on the least squares based squared residuals of the predictive regression and is straightforward to implement. The distributions of the two test statistics we introduce are shown to be free of nuisance parameters, valid under dependent errors, already tabulated in the literature and robust to the degree of persistence of the chosen predictor. Our proposed method is subsequently applied to the predictability of US stock returns.
851-874
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
October 2017
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
Pitarakis, Jean-Yves
(2017)
A simple approach for diagnosing instabilities in predictive regressions.
Oxford Bulletin of Economics and Statistics, 79 (5), .
(doi:10.1111/obes.12184).
Abstract
We introduce a method for detecting the presence of structural breaks in the parameters of predictive regressions linking noisy variables such as stock returns to persistent predictors such as valuation ratios. Our approach relies on the least squares based squared residuals of the predictive regression and is straightforward to implement. The distributions of the two test statistics we introduce are shown to be free of nuisance parameters, valid under dependent errors, already tabulated in the literature and robust to the degree of persistence of the chosen predictor. Our proposed method is subsequently applied to the predictability of US stock returns.
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Accepted/In Press date: 8 March 2017
e-pub ahead of print date: 5 April 2017
Published date: October 2017
Organisations:
Economics
Identifiers
Local EPrints ID: 406844
URI: http://eprints.soton.ac.uk/id/eprint/406844
ISSN: 0305-9049
PURE UUID: 5d5ed0d3-c097-4952-8a78-8d95f15843ff
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Date deposited: 24 Mar 2017 02:03
Last modified: 16 Mar 2024 05:09
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