Economic and Statistical Measures of Forecast Accuracy

Clive W. J. Granger and M Hashem Pesaran ()

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: This paper argues in favour of a closer link between decision and forecast evaluation problems. Although the idea of using decision theory for forecast evaluation appears early in the dynamic stochastic programming literature, and has continued to be used in meteorological forecasts, it is hardly mentioned in standard academic textbooks on economic forecasting. Some of the main issues involved are illustrated in the context of a two-state, two-action decision problem as well as in a more general setting. Relationships between statistical and economic methods of forecast evaluation are discussed and useful links between Kuipers score, used as a measure of forecast accuracy in the meteorology literature, and the market timing tests used in finance, are established. An empirical application to the problem of stock market predictability is also provided, and the conditions under which such predictability could be exploited in the presence of transaction costs are discussed.

Keywords: Decision theory; Forecast evaluation; Probabilistsic forecasts; Economic and statistical measures of forecast accuracy; Stock market predictability (search for similar items in EconPapers)
JEL-codes: C12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 1999-05
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Persistent link: http://EconPapers.repec.org/RePEc:cam:camdae:9910

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