Random-Time Aggregation in Partial Adjustment Models
研究数据按固定时间间隔收集但经济决策在随机时间发生对部分调整模型计量分析的影响，发现调整速度估计有偏、外生变量相关性被低估，并导致状态依赖时间序列模型。
How is econometric analysis (of partial adjustment models) affected by the fact that, although data collection is done at regular, fixed intervals of time, economic decisions are made at random intervals of time? This article addresses this question by modeling the economic decision-making process as a general point process. Under random-time aggregation, (1) inference on the speed of adjustment is biased—adjustments are a function of the intensity of the point process and the proportion of adjustment—(2) inference on the correlation with exogenous variables is generally downward biased, and (3) a nonconstant intensity of the point process gives rise to a general class of regime-dependent time series models. An empirical application to test the production-smoothing-buffer-stock model of inventory behavior illustrates, in practice, the effects of random-time aggregation.