On the optimality of expert-adjusted forecasts
Official forecasts of international institutions are never purely model-based. Preliminary results of models are adjusted with expert opinions. What is the impact of these adjustments for the forecasts? Are they necessary to get 'optimal' forecasts? When model-based forecasts are adjusted by experts, the loss function of these forecasts is not a mean squared error loss function. In fact, the overall loss function is unknown. To examine the quality of these forecasts, one can rely on the tests for forecast optimality under unknown loss function as developed in Patton and Timmermann (2007). We apply one of these tests to ten variables for which we have model-based forecasts and expert-adjusted forecasts, all generated by the Netherlands Bureau for Economic Policy Analysis (CPB). For almost all variables the added expertise yields better forecasts in terms of fit. In terms of optimality, the effect of adjustments for the forecasts is limited, because for most variables the assumption that the forecast are not optimal can be rejected for both the model-based and the expert-adjusted forecasts.
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- Clements, Michael P, 1995. "Rationality and the Role of Judgement in Macroeconomic Forecasting," Economic Journal, Royal Economic Society, vol. 105(429), pages 410-20, March.
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- repec:cup:cbooks:9780521634809 is not listed on IDEAS
- Patton, Andrew J. & Timmermann, Allan, 2007. "Testing Forecast Optimality Under Unknown Loss," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1172-1184, December.
- Henk Kranendonk & Johan Verbruggen, 2007. "SAFFIER; a multi-purpose model of the Dutch economy for short-term and medium-term analyses," CPB Document 144, CPB Netherlands Bureau for Economic Policy Analysis.
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