Economic and statistical measures of forecast accuracy

This paper argues in favour of a closer link between the decision and the 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 with meteorological forecasts, it is hardly mentioned in standard academic texts 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 links between the 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 explained in the presence of transaction costs are discussed.