Benchmarking Judgmentally Adjusted Forecasts
提出一种评估判断调整后预测质量的方法，通过两种基准预测（无变化预测和模型预测）来学习过去误差，并应用于美国和荷兰的GDP增长预测。
Abstract Many publicly available macroeconomic forecasts are judgmentally adjusted model‐based forecasts. In practice, usually only a single final forecast is available, and not the underlying econometric model, nor are the size and reason for adjustment known. Hence, the relative weights given to the model forecasts and to the judgement are usually unknown to the analyst. This paper proposes a methodology to evaluate the quality of such final forecasts, also to allow learning from past errors. To do so, the analyst needs benchmark forecasts. We propose two such benchmarks. The first is the simple no‐change forecast, which is the bottom line forecast that an expert should be able to improve. The second benchmark is an estimated model‐based forecast, which is found as the best forecast given the realizations and the final forecasts. We illustrate this methodology for two sets of GDP growth forecasts, one for the USA and one for the Netherlands. These applications tell us that adjustment appears most effective in periods of first recovery from a recession. Copyright © 2016 John Wiley & Sons, Ltd.