Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data
讨论用对称标准化GMM和有限信息最大似然法估计线性面板数据模型，这些方法在弱工具变量下有限样本偏差更小，并通过模拟和英西企业数据展示其优势。
Abstract We discuss the estimation of linear panel-data models with sequential moment restrictions using symmetrically normalized generalized method of moments (GMM) estimators (SNM) and limited information maximum likelihood (LIML) analogues. These estimators are asymptotically equivalent to standard GMM but are invariant to normalization and tend to have a smaller finite-sample bias, especially when the instruments are poor. We study their properties in relation to ordinary GMM and minimum distance estimators for AR(1) models with individual effects by mean of simulations. Finally, as empirical illustrations, we estimate by SNM and LIML employment and wage equations using panels of U.K. and Spanish firms. KEY WORDS: Autoregressive modelsDynamic panel dataEmployment equationsGeneralized method of momentsMonte Carlo methodsSymmetric normalization