Random-Time Aggregation in Partial Adjustment Models

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.