Forecast Evaluation with Shared Data Sets

Ryan Sullivan, Allan Timmermann () and Halbert White

No 3060, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: Data sharing is common practice in forecasting experiments in situations where fresh data samples are difficult or expensive to generate. This means that forecasters often analyze the same data set using a host of different models and sets of explanatory variables. This practice introduces statistical dependencies across forecasting studies that can severely distort statistical inference. Here we examine a new and inexpensive recursive bootstrap procedure that allows forecasters to account explicitly for these dependencies. The procedure allows forecasters to merge empirical evidence and draw inference in the light of previously accumulated results. In an empirical example, we merge results from predictions of daily stock prices based on (1) technical trading rules and (2) calendar rules, demonstrating both the significance of problems arising from data sharing and the simplicity of accounting for data sharing using these new methods.

Keywords: bootstrap; calendar effects; data sharing; forecast evaluation; technical trading (search for similar items in EconPapers)
JEL-codes: C10 (search for similar items in EconPapers)
Date: 2001-11

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Journal Article: Forecast evaluation with shared data sets (2003)
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