Two-Step and Related Estimators in Contemporary Rational-Expectations Models: An Analysis of Small-Sample Properties
比较了普通最小二乘、广义最小二乘和Pagan双倍长度估计量在几种理性预期模型中的小样本表现，发现双倍长度估计量在大多数情况下优于最小二乘，但最小二乘在极小样本中更可靠。
Abstract This article examines the performance of ordinary least squares, generalized least squares, and Pagan's (1986) double-length estimator (DLE) in several rational-expectations models. The three approaches are equivalent in the simplest of models but may differ appreciably in models typically encountered in applied work. Small-sample properties of the estimators are examined in several contemporary macroeconomic models. The following conclusions are reached: (a) All estimators exhibit similar sampling distributions in a monetary-neutrality framework, (b) the least squares procedures maintain smaller sampling variance and deliver more reliable tests in a permanent-income model in very small samples, (c) DLE generally delivers superior performance in a nonlinear aggregate-supply model with unanticipated "shock" regressors, and (d) overall, DLE outperforms the LS alternatives except in the smallest of samples. KEY WORDS: Generalized least squaresGenerated regressorsMonte Carlo simulationNonlinear estimation