The Properties of AutomaticGetsModelling
回顾了PcGets中从一般到特殊的自动回归模型选择的模拟表现，展示了模型选择如何做到无偏估计、标准误接近抽样标准差，并处理理论约束、非平稳性、共线性及三个棘手问题。
After reviewing the simulation performance of general‐to‐specific automatic regression‐model selection, as embodied in PcGets, we show how model selection can be non‐distortionary: approximately unbiased ‘selection estimates’ are derived, with reported standard errors close to the sampling standard deviations of the estimated DGP parameters, and a near‐unbiased goodness‐of‐fit measure. The handling of theory‐based restrictions, non‐stationarity and problems posed by collinear data are considered. Finally, we consider how PcGets can handle three ‘intractable’ problems: more variables than observations in regression analysis; perfectly collinear regressors; and modelling simultaneous equations without a priori restrictions.