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Forecast evaluation with shared data sets
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DOI：10.1016/S0169-2070(01)00140-6.png)
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Abstract
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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 a-rising from data sharing and the simplicity of accounting for data sharing using these new methods. (C) 2001 International Institute of Forecasters. Published by Elsevier Science B.V. All rights reserved.
Keywords:
forecast evaluation
bootstrap
data sharing
calendar effects
technical trading
data mining
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