Estimating Parameters of the Autocorrelated Current Effects Model from Temporally Aggregated Data
作者回顾了自相关当期效应模型的相关文献，并提出了一种简单方法，能在已知聚合水平的情况下，从时间聚合数据中恢复该模型的参数。该方法部分基于一阶自相关系数的估计。作者通过蒙特卡洛实验展示了该方法的操作，并将其性质与广义最小二乘法（GLS）进行了比较。在多数测试案例中，使用聚合数据能较好地恢复微观参数。
The authors briefly review the literature associated with the autocorrelated current effects model and present a simple procedure that recovers its parameters from time-aggregated data when the level of aggregation is known. The procedure is based partly on the estimation of a first-order autocorrelation coefficient. The procedure is illustrated and its properties are compared with those of a GLS procedure by means of a Monte Carlo experiment. In many of the tested cases, there is good recovery of the microparameters with aggregated data.