DAVID HARVEY dave.harvey@nottingham.ac.uk
Professor of Econometrics
Forecast evaluation tests and negative long-run variance estimates in small samples
Harvey, David I.; Leybourne, Stephen J.; Whitehouse, Emily J.
Authors
STEVE LEYBOURNE steve.leybourne@nottingham.ac.uk
Professor of Econometrics
Emily J. Whitehouse
Abstract
In this paper, we show that when computing standard Diebold-Mariano-type tests for equal forecast accuracy and forecast encompassing, the long-run variance can frequently be negative when dealing with multi-step-ahead predictions in small, but empirically relevant, sample sizes. We subsequently consider a number of alternative approaches to dealing with this problem, including direct inference in the problem cases and use of long-run variance estimators that guarantee positivity. The finite sample size and power of the different approaches are evaluated using extensive Monte Carlo simulation exercises. Overall, for multi-step-ahead forecasts, we find that the recently proposed Coroneo and Iacone (2016) test, which is based on a weighted periodogram long-run variance estimator, offers the best finite sample size and power performance.
Citation
Harvey, D. I., Leybourne, S. J., & Whitehouse, E. J. (2017). Forecast evaluation tests and negative long-run variance estimates in small samples. International Journal of Forecasting, 33(4), https://doi.org/10.1016/j.ijforecast.2017.05.001
Journal Article Type | Article |
---|---|
Acceptance Date | May 3, 2017 |
Online Publication Date | Jun 15, 2017 |
Publication Date | Nov 1, 2017 |
Deposit Date | May 23, 2017 |
Publicly Available Date | Jun 16, 2019 |
Journal | International Journal of Forecasting |
Print ISSN | 0169-2070 |
Electronic ISSN | 0169-2070 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 33 |
Issue | 4 |
DOI | https://doi.org/10.1016/j.ijforecast.2017.05.001 |
Keywords | Forecast evaluation; Long-run variance estimation; Simulation; Diebold-Mariano test; Forecasting |
Public URL | https://nottingham-repository.worktribe.com/output/892023 |
Publisher URL | http://www.sciencedirect.com/science/article/pii/S0169207017300559 |
Contract Date | May 23, 2017 |
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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0
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