Small-Sample Properties of GMM for Business-Cycle Analysis
通过蒙特卡洛方法研究广义矩方法在商业周期分析中的小样本性质，发现对于战后美国季度数据规模的样本，现有渐近理论并不适用。
We investigate, by Monte Carlo methods, the finite-sample properties of generalized method of moment procedures for conducting inference about statistics that are of interest in the business-cycle literature. These statistics include the second moments of data filtered using the first-difference and Hodrick–Prescott filters, and they include statistics for evaluating model fit. Our results indicate that, for the procedures considered, the existing asymptotic theory is not a good guide in a sample the size of quarterly postwar U.S. data.