In this paper, we use automated algorithms to update and evaluate ad hoc judgments that are made in even the most basic estimations. Such an application of an automated algorithm robustifies empirical econometric analyses, it achieves lower and more consistent prediction errors, and it helps to prevent data snooping. Tools are introduced to evaluate the algorithm, to see how configurations were updated by the algorithm, to study how forecasting accuracy is affected the choice of configurations, and to find out which configurations can safely be ignored in order to increase the speed of the algorithm. In our case study, we develop an algorithm that updates ad hoc judgments that are made in Cápistran and Timmermann's attempt to beat the mean survey forecast. Many of these ad hoc judgments are often made in time series forecasting and have hitherto been overlooked. We show that the algorithm improves their models and we further robustify the stylized fact that the mean survey forecast is difficult to beat.
|Publication status||Published - 2013|