Temporal Aggregation and Economic Time Series

We examine the effects of temporal aggregation on the estimated time series properties of economic data. Theory predicts that temporal aggregation loses information about the underlying data processes. We find those losses to be substantial. Monthly and quarterly data are governed by complex time series processes with much low-frequency cyclical variation, whereas annual data are governed by extremely simple processes with virtually no cyclical variation. Cycles of much more than a year's duration in the monthly data disappear when the data are aggregated to annual observations. Moreover, the aggregated data show more long-run persistence than the underlying disaggregated data.