Out-of-Sample Forecast Tests Robust to the Choice of Window Size
AbstractThis article proposes new methodologies for evaluating economic models’ out-of-sample forecasting performance that are robust to the choice of the estimation window size. The methodologies involve evaluating the predictive ability of forecasting models over a wide range of window sizes. The study shows that the tests proposed in the literature may lack the power to detect predictive ability and might be subject to data snooping across different window sizes if used repeatedly. An empirical application shows the usefulness of the methodologies for evaluating exchange rate models’ forecasting ability.
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Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of Business & Economic Statistics.
Volume (Year): 30 (2012)
Issue (Month): 3 (April)
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Web page: http://www.amstat.org/publications/jbes/index.cfm?fuseaction=main
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- Peter Reinhard Hansen & Allan Timmermann, 2012.
"Equivalence Between Out-of-Sample Forecast Comparisons and Wald Statistics,"
CREATES Research Papers
2012-45, School of Economics and Management, University of Aarhus.
- Peter Reinhard Hansen & Allan Timmermann, 2012. "Equivalence Between Out-of-Sample Forecast Comparisons and Wald Statistics," Economics Working Papers ECO2012/24, European University Institute.
- Richard A. Ashley & Kwok Ping Tsang, 2013. "Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach," Working Papers e07-40, Virginia Polytechnic Institute and State University, Department of Economics.
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