Economic and Statistical Measures of Forecast Accuracy
AbstractThis 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.
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Bibliographic InfoPaper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 9910.
Date of creation: May 1999
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Web page: http://www.econ.cam.ac.uk/index.htm
Decision theory; Forecast evaluation; Probabilistsic forecasts; Economic and statistical measures of forecast accuracy; Stock market predictability;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2000-01-24 (All new papers)
- NEP-ECM-2000-01-24 (Econometrics)
- NEP-ETS-2000-01-24 (Econometric Time Series)
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