Dissecting models' forecasting performance |
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Institution: | 1. Kyung Hee University, Republic of Korea;2. KISDI (Korea Information Society Development Institute), Republic of Korea;3. University of Otago, New Zealand;4. University of Newcastle, Australia;1. Department of Agricultural Economics, Humboldt-Universität zu Berlin, Germany;2. European Commission, Joint Research Centre, Institute for Prospective Technological Studies (IPTS), Spain;3. Institute of Agricultural Policy and Markets (420), Universität Hohenheim, 70593 Stuttgart, Germany;4. Institute of Applied Mathematics and Statistics (110), Universität Hohenheim, 70599 Stuttgart, Germany |
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Abstract: | The fact that the predictive performance of models used in forecasting stock returns, exchange rates, and macroeconomic variables is not stable and varies over time has been widely documented in the forecasting literature. Under these circumstances excessive reliance on forecast evaluation metrics that ignores this instability in forecasting accuracy, like squared errors averaged over the whole forecast evaluation sample, masks important information regarding the temporal evolution of relative forecasting performance of competing models. In this paper we suggest an approach based on the combination of the Cumulated Sum of Squared Forecast Error Differential (CSSFED) of Welch and Goyal (2008) and the Bayesian change point analysis of Barry and Hartigan (1993) that tracks the contribution of forecast errors to the aggregate measures of forecast accuracy observation by observation. In doing so, it allows one to track the evolution of the relative forecasting performance over time. We illustrate the suggested approach by using forecasts of the GDP growth rate in Switzerland. |
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Keywords: | Forecasting Forecast evaluation Change point detection Bayesian estimation |
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