不等长度样本的广义矩法

A. Lynch, Jessica A. Wachter
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引用次数: 0

摘要

本文将Hansen(1982)的广义矩技术方法扩展到在不同样本周期内观察矩条件的情况。金融经济学中的许多应用程序使用具有不同起始日期的数据序列,或者更罕见的是不同结束日期的数据序列。通常的做法是取观测数据的样本周期的交集;然后交叉点成为研究的样本周期,其余数据被忽略。本文描述了一种替代方案,允许研究人员利用每个时刻条件的所有可用数据。我们描述了两个渐近等价估计,它们是一致的,渐近正态的,并且比标准GMM更有效。首先使用全样本上的样本平均值来估计全样本数据可用的矩,然后使用短样本上的样本平均值来估计只有短样本数据可用的矩,然后使用短样本矩对全样本矩的回归系数来调整短样本矩。第二种方法使用全样本矩可用的数据的非重叠段来形成一组额外的矩条件。我们将这两个估计器扩展到具有更一般的缺失数据模式的设置。我们证明了扩展估计量是渐近等价的,一致的,渐近正态的,并且渐近地比忽略部分样本的估计量更可靠,无论它是否被观察到所有序列。通过暗示,扩展估计量比标准GMM更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generalized Method of Moments for Samples of Unequal Length
This paper extends the generalized method of moments technique of Hansen (1982) to cases where moment conditions are observed over different sample periods. Many applications in financial economics use data series that have different starting dates, or, more rarely, different ending dates. Common practice is to take the intersection of the sample periods over which the data are observed; the intersection then becomes the sample period for the study and the rest of the data are ignored. This paper describes an alternative that allows the researcher to make use of all of the data available for each moment condition. We describe two asymptotically equivalent estimators that are consistent, asymptotically normal, and more efficient asymptotically than standard GMM. The first uses sample averages over the full sample to estimate the moments for which full-sample data are available, and sample averages over the short sample to estimate moments for which only the short-sample data are available, and then adjusts the short-sample moment using coefficients from a regression of the short-sample moments on the full-sample moments. The second uses the non-overlapping segment of the data available for the full-sample moments to form an additional set of moment conditions. We extend both of these estimators to settings with more general patterns of missing data. We show that the extended estimators are asymptotically equivalent, consistent, asymptotically normal, and asymptotically more e±cient than estimators that ignore a portion of the sample, whether or not it is observed for all series. By implication, the extended estimators are more efficient than standard GMM.
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