Temporal Aggregation and Economic Time Series
Temporal Aggregation and Economic Time Series
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时间聚合和经济时间序列
DOI：
10.1080/07350015.1995.10524618
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发表时间：
1995
影响因子：
3
通讯作者：
John J. Seater
中科院分区：
文献类型：
--
作者：
Robert J. Rossana;John J. Seater
作者研究了时间聚集对经济数据估计时间序列特性的影响。理论预测时间聚合会丢失关于底层数据处理的信息。提交人认为这些损失是巨大的。月度和季度数据由复杂的时间序列过程控制，其中有许多低频周期性变化，而年度数据由极其简单的过程控制，几乎没有周期性变化。当数据汇总为年度观测数据时，月度数据中持续时间远远超过一年的周期消失了。此外，汇总数据比基础的分类数据显示出更强的长期持久性。
The authors examine the effects of temporal aggregation on the estimated time-series properties of economic data. Theory predicts temporal aggregation loses information about the underlying data processes. The authors 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. Also, the aggregated data show more long-run persistence than the underlying disaggregated data.