Out of sample predictability in predictive regressions with many predictor candidates
Out of sample predictability in predictive regressions with many predictor candidates
This paper is concerned with detecting the presence of out-of-sample predictability in linear predictive regressions with a potentially large set of candidate predictors. We propose a procedure based on out-of-sample MSE comparisons that is implemented in a pairwise manner using one predictor at a time. This results in an aggregate test statistic that is standard normally distributed under the global null hypothesis of no linear predictability. Predictors can be highly persistent, purely stationary, or a combination of both. Upon rejecting the null hypothesis, we introduce a predictor screening procedure designed to identify the most active predictors. An empirical application to key predictors of US economic activity illustrates the usefulness of our methods. It highlights the important forward-looking role played by the series of manufacturing new orders.
Forecasting, High dimensional predictability, Nested models, Out-of-sample, Predictive regression
Gonzalo, Jesús
48015f9d-eef0-4ebd-8f2b-cbe7aa0cf667
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
Gonzalo, Jesús
48015f9d-eef0-4ebd-8f2b-cbe7aa0cf667
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
Gonzalo, Jesús and Pitarakis, Jean-Yves
(2023)
Out of sample predictability in predictive regressions with many predictor candidates.
International Journal of Forecasting.
(doi:10.1016/j.ijforecast.2023.10.005).
Abstract
This paper is concerned with detecting the presence of out-of-sample predictability in linear predictive regressions with a potentially large set of candidate predictors. We propose a procedure based on out-of-sample MSE comparisons that is implemented in a pairwise manner using one predictor at a time. This results in an aggregate test statistic that is standard normally distributed under the global null hypothesis of no linear predictability. Predictors can be highly persistent, purely stationary, or a combination of both. Upon rejecting the null hypothesis, we introduce a predictor screening procedure designed to identify the most active predictors. An empirical application to key predictors of US economic activity illustrates the usefulness of our methods. It highlights the important forward-looking role played by the series of manufacturing new orders.
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Gonzalo and Pitarakis Accepted Paper and Supplementary Appendix
- Accepted Manuscript
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More information
Accepted/In Press date: 13 October 2023
e-pub ahead of print date: 28 October 2023
Additional Information:
Funding Information:
The authors gratefully acknowledge financial support from the Spanish Ministerio de Ciencia e Innovación through grants PID2019-104960GB-I00 , TED2021-129784B-I00 , AEI/10.13039/501100011033-CEX2021-001181-M , and the UK Economic and Social Research Council through grant ES/W000989/1 .
Publisher Copyright:
© 2023 The Authors
Keywords:
Forecasting, High dimensional predictability, Nested models, Out-of-sample, Predictive regression
Identifiers
Local EPrints ID: 483487
URI: http://eprints.soton.ac.uk/id/eprint/483487
ISSN: 0169-2070
PURE UUID: 02890271-2d3b-4c1a-80d9-813b1fd40ffe
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Date deposited: 31 Oct 2023 18:16
Last modified: 18 Mar 2024 02:57
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Author:
Jesús Gonzalo
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