论文标题
测试GMM和M估计器的一致性和根N渐近正态性的有限矩条件
Testing Finite Moment Conditions for the Consistency and the Root-N Asymptotic Normality of the GMM and M Estimators
论文作者
论文摘要
经验经济分析中结构和减少形式参数的推断的常见方法是基于GMM和M估计量的一致性和根 - N渐近正态性。这些类别的估计量的规范一致性（分别为根 - 渐近正态性）至少需要分数的第一个（第二个）时刻为有限。在本文中，我们提出了一种测试这些条件的方法，以达到GMM和M估计量的一致性和根 - N渐近正态性。所提出的测试在与零假设兼容的数据生成过程的集合中几乎均匀地控制大小。仿真研究支持这一理论结果。将拟议的测试应用于Dominick较精美的食品零售链的市场份额数据，我们发现一种常见的\ textit {Ad hoc}程序可以处理零市场份额，以分析差异化产品市场的分析导致无法满足一致性和根 - nrymptotic正常性的条件。
Common approaches to inference for structural and reduced-form parameters in empirical economic analysis are based on the consistency and the root-n asymptotic normality of the GMM and M estimators. The canonical consistency (respectively, root-n asymptotic normality) for these classes of estimators requires at least the first (respectively, second) moment of the score to be finite. In this article, we present a method of testing these conditions for the consistency and the root-n asymptotic normality of the GMM and M estimators. The proposed test controls size nearly uniformly over the set of data generating processes that are compatible with the null hypothesis. Simulation studies support this theoretical result. Applying the proposed test to the market share data from the Dominick's Finer Foods retail chain, we find that a common \textit{ad hoc} procedure to deal with zero market shares in analysis of differentiated products markets results in a failure to satisfy the conditions for both the consistency and the root-n asymptotic normality.
