On the optimality of expert-adjusted forecasts

Official forecasts of international institutions are never purely model-based. The 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). We find that 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 are 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.