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

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dc.contributor.author HANSEN, Peter Reinhard
dc.contributor.author TIMMERMANN, Allan
dc.date.accessioned 2012-04-02T07:45:12Z
dc.date.available 2012-04-02T07:45:12Z
dc.date.issued 2012
dc.identifier.issn 1725-6704
dc.identifier.uri http://hdl.handle.net/1814/21454
dc.description.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. en
dc.language.iso en en
dc.relation.ispartofseries EUI ECO en
dc.relation.ispartofseries 2012/10 en
dc.subject Out-of-sample forecast evaluation en
dc.subject data mining en
dc.subject recursive estimation en
dc.subject predictability of stock returns en
dc.subject inflation forecasting en
dc.subject C12 en
dc.subject C53 en
dc.title Choice of Sample Split in Out-of-Sample Forecast Evaluation en
dc.type Working Paper en
dc.neeo.contributor HANSEN|Peter Reinhard|aut|EUI70016
dc.neeo.contributor TIMMERMANN|Allan|aut|


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