Two-Step Generalized Least Squares Estimators in Multi-Equation Generated Regressor Models
针对含期望的实证模型中回归变量代理被当作非随机变量处理的问题，提出一种两步广义最小二乘估计量，作为Pagan(1984)分析的自然扩展，适用于多方程模型，并通过数值实验说明忽略该问题的严重性。
Despite the critical analysis of Pagan (1984) and several subsequent applied studies, empirical models characterized by expectations are often estimated with regressor proxies that are treated as ordinary nonstochastic This paper offers a Generalized Least Squares estimator designed to cope with the nonscalar disturbance matrix precipatated by generated The approach is designed as a natural extension of Pagan's analysis and the author demonstrates how it may be applied to multi-equation models. Experimentation with numerical examples reveals the potential severity of ignoring the problem. These results also suggest an easily calculated indicator of potential inference distortion in models that fail to account for regressors. Copyright 1987 by MIT Press.