Whimsical aggregation of temporal data, market identification, and fragile results

Abstract

One of the most common problems faced by analysts of agribusiness markets is that available data are aggregated to a degree that obscures the underlying decision process. This article reminds analysts of the implications of temporal data aggregation for market identification and its effects on the robustness of empirical results. Also, three major commodity market price series are analyzed to demonstrate how aggregation can affect empirical results. Finally, guidelines are suggested for selecting the appropriate level of aggregation for empirical problems.