A novel approach to predictive accuracy testing in nested environments
A novel approach to predictive accuracy testing in nested environments
We introduce a new approach for comparing the predictive accuracy of two nested models that bypasses the difficulties caused by the degeneracy of the asymptotic variance of forecast error loss differentials used in the construction of commonly used predictive comparison statistics. Our approach continues to rely on the out of sample mean squared error loss differentials between the two competing models, leads to nuisance parameter-free Gaussian asymptotics, and is shown to remain valid under flexible assumptions that can accommodate heteroskedasticity and the presence of mixed predictors (e.g., stationary and local to unit root). A local power analysis also establishes their ability to detect departures from the null in both stationary and persistent settings. Simulations calibrated to common economic and financial applications indicate that our methods have strong power with good size control across commonly encountered sample sizes.
Forecasting, Nested Models, Variance degeneracy, MSE differentials
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
Pitarakis, Jean-Yves
(2023)
A novel approach to predictive accuracy testing in nested environments.
Econometric Theory.
(doi:10.1017/S0266466623000154).
Abstract
We introduce a new approach for comparing the predictive accuracy of two nested models that bypasses the difficulties caused by the degeneracy of the asymptotic variance of forecast error loss differentials used in the construction of commonly used predictive comparison statistics. Our approach continues to rely on the out of sample mean squared error loss differentials between the two competing models, leads to nuisance parameter-free Gaussian asymptotics, and is shown to remain valid under flexible assumptions that can accommodate heteroskedasticity and the presence of mixed predictors (e.g., stationary and local to unit root). A local power analysis also establishes their ability to detect departures from the null in both stationary and persistent settings. Simulations calibrated to common economic and financial applications indicate that our methods have strong power with good size control across commonly encountered sample sizes.
Text
Pitarakis ET Accepted Paper April 2023
- Accepted Manuscript
More information
Accepted/In Press date: 17 April 2023
e-pub ahead of print date: 17 May 2023
Additional Information:
Funding Information:
I wish to thank the Editor, Co-Editor, and three anonymous referees for the quality of the reports I have received and their in-depth review of an earlier version of this paper. I also wish to thank the ESRC for its financial support via grant ES/W000989/1. Any errors are my own responsibility.
Publisher Copyright:
© The Author(s), 2023. Published by Cambridge University Press.
Keywords:
Forecasting, Nested Models, Variance degeneracy, MSE differentials
Identifiers
Local EPrints ID: 476877
URI: http://eprints.soton.ac.uk/id/eprint/476877
ISSN: 0266-4666
PURE UUID: 08e46071-7d6a-4eab-8b16-3d55b8729ebf
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Date deposited: 18 May 2023 16:54
Last modified: 17 Mar 2024 02:57
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