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Choice of Sample Split in Out-of-Sample Forecast Evaluation

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  • Peter Reinhard HANSEN
  • Allan TIMMERMANN

Abstract

Out-of-sample tests of forecast performance depend on how a given data set is split into estimation and evaluation periods, yet no guidance exists on how to choose the split point. Empirical forecast evaluation results can therefore be di cult to interpret, particularly when several values of the split point might have been considered. When the sample split is viewed as a choice variable, rather than being fixed ex ante, we show that very large size distortions can occur for conventional tests of predictive accuracy. Spurious rejections are most likely to occur with a short evaluation sample, while conversely the power of forecast evaluation tests is strongest with long out-of-sample periods. To deal with size distortions, we propose a test statistic that is robust to the effect of considering multiple sample split points. Empirical applications to predictability of stock returns and inflation demonstrate that out-of-sample forecast evaluation results can critically depend on how the sample split is determined.

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Bibliographic Info

Paper provided by European University Institute in its series Economics Working Papers with number ECO2012/10.

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Date of creation: 2012
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Handle: RePEc:eui:euiwps:eco2012/10

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Keywords: C12; C53;

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Citations

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Cited by:
  1. Agnieszka Markiewicz & Andreas Pick, 2013. "Adaptive Learning and Survey Data," CDMA Working Paper Series 201305, Centre for Dynamic Macroeconomic Analysis.
  2. Fang, Jiali & Jacobsen, Ben & Qin, Yafeng, 2014. "Predictability of the simple technical trading rules: An out-of-sample test," Review of Financial Economics, Elsevier, vol. 23(1), pages 30-45.
  3. repec:wyi:journl:002213 is not listed on IDEAS
  4. Hutter, Christian & Weber, Enzo, 2013. "Constructing a new leading indicator for unemployment from a survey among German employment agencies," IAB Discussion Paper 201317, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  5. Wang, Cindy Shin-Huei & Bauwens, Luc & Hsiao, Cheng, 2013. "Forecasting a long memory process subject to structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 171-184.
  6. Peter Reinhard Hansen & Allan Timmermann, 2012. "Equivalence Between Out-of-Sample Forecast Comparisons and Wald Statistics," Economics Working Papers ECO2012/24, European University Institute.
  7. He, Kaijian & Wang, Lijun & Zou, Yingchao & Lai, Kin Keung, 2014. "Value at risk estimation with entropy-based wavelet analysis in exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 62-71.
  8. Biqing Cai & Jiti Gao, 2013. "Hermite Series Estimation in Nonlinear Cointegrating Models," Monash Econometrics and Business Statistics Working Papers 17/13, Monash University, Department of Econometrics and Business Statistics.
  9. repec:dgr:uvatin:2012110 is not listed on IDEAS

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