Temporal Aggregation and Economic Time Series
研究时间加总对经济数据时间序列性质的影响，发现月度与季度数据包含复杂低频周期，而年度数据丢失大量周期信息且显示更强长期持久性。
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.