Identification Issues in Forward-Looking Models Estimated by GMM, with an Application to the Phillips Curve
指出用GMM估计前瞻性理性预期模型（如新凯恩斯菲利普斯曲线）时，因单方程设定不完整而忽视的识别问题，包括弱工具变量、识别与动态误设的关联，以及过度识别检验效力不足，提醒研究者关注这些实证难题。
Limited-information methods are commonly used to estimate forwardlooking models with rational expectations, such as the "New Keynesian Phillips Curve" of Galí and Gertler (1999). In this paper, we address issues of identification that have been overlooked due to the incompleteness of the single-equation formulation. We show that problems of weak instruments may arise, depending on the properties of the 'exogenous' variables, and that they are empirically relevant. We also uncover a link between identification and dynamic mis-specification, and examine the (lack of) power of Hansen's (1982) J test to detect invalid over-identifying restrictions. With regards to the New Phillips curve, we find that problems of identification cannot be ruled out, and they deserve further attention.