GMM的广义集中信息准则
A Generalized Focused Information Criterion for GMM
摘要：
本文提出了一种同时选择GMM模型和矩的准则：广义聚焦信息准则（GFIC）。GFIC不是试图识别“真实”规范，而是从一组可能错误指定的力矩条件和参数限制中进行选择，以最小化用户指定的目标参数的均方误差（MSE）。GFIC的目的是将应用实践中常见的情况正式化。一个应用研究者从一组相当弱的“基线”假设开始，假设是正确的，并且必须决定是否施加一些更强的、更有争议的“可疑”假设中的任何一个，这些假设产生参数限制、附加力矩条件或两者兼而有之。假设基线假设识别模型，我们将展示如何构建渐近MSE的渐近无偏估计量来选择这些可疑假设：GFIC。我们继续提供选择后的推断和模型平均，可以应用到GFIC和各种替代选择标准的结果。为了说明我们的标准可以在实践中使用，我们专门的GFIC选择外生性假设和滞后长度的动态面板模型中的问题，并表明它在模拟中表现良好。最后，我们将GFIC应用于卷烟需求价格弹性的动态面板数据模型。
英文摘要：
This paper proposes a criterion for simultaneous GMM model and moment selection: the generalized focused information criterion (GFIC). Rather than attempting to identify the "true" specification, the GFIC chooses from a set of potentially mis-specified moment conditions and parameter restrictions to minimize the mean-squared error (MSE) of a user-specified target parameter. The intent of the GFIC is to formalize a situation common in applied practice. An applied researcher begins with a set of fairly weak "baseline" assumptions, assumed to be correct, and must decide whether to impose any of a number of stronger, more controversial "suspect" assumptions that yield parameter restrictions, additional moment conditions, or both. Provided that the baseline assumptions identify the model, we show how to construct an asymptotically unbiased estimator of the asymptotic MSE to select over these suspect assumptions: the GFIC. We go on to provide results for post-selection inference and model averaging that can be applied both to the GFIC and various alternative selection criteria. To illustrate how our criterion can be used in practice, we specialize the GFIC to the problem of selecting over exogeneity assumptions and lag lengths in a dynamic panel model, and show that it performs well in simulations. We conclude by applying the GFIC to a dynamic panel data model for the price elasticity of cigarette demand.
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