摘要翻译：
研究了动态离散选择对策中结构参数的一类估计的渐近性质。我们考虑K阶段策略迭代(PI)估计，其中K表示在估计中使用的策略迭代次数。这个类包含了一些文献中提出的估计量，如Aguirregabiria和Mira(2002，2007)、Pesendorfer和Schmidt-Dengler(2008)和Pakes等人的估计量。（2007年）。首先，我们证明了K-PML估计量对于所有K是相合的和渐近正态的，这补充了Aguirregabiria和Mira(2007)的研究结果，他们的研究重点是K=1和K大到足以引起估计量收敛。在一定的条件下，我们证明了K-PML估计的渐近方差可以作为K的函数表现出任意的模式。其次，我们证明了K-MD估计对于所有K是相合的和渐近正态的。对于一个特定的权矩阵，K-MD估计具有与K-PML估计相同的渐近分布。我们的主要结果给出了K-MD估计的最优权矩阵序列，并证明了最优加权K-MD估计具有对K不变的渐近分布。给出的不变性结果特别出乎意料地给出了Aguirregabiria和Mira(2007)关于K-PML估计的发现。我们的主要结果包含了关于最优1-MD估计量（由Pesendorfer和Schmidt-Dengler(2008)导出）的两个新推论。首先，最优1-MD估计在K-MD估计类中是最优的。换句话说，附加的策略迭代不能提供相对于最优1-MD估计量的渐近效率增益。其次，对于所有K,最优1-MD估计比任何K-PML估计都是渐近有效的。最后，附录给出了最优1-MD估计渐近有效的适当条件。
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英文标题：
《On the iterated estimation of dynamic discrete choice games》
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作者：
Federico A. Bugni and Jackson Bunting
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  We study the asymptotic properties of a class of estimators of the structural parameters in dynamic discrete choice games. We consider K-stage policy iteration (PI) estimators, where K denotes the number of policy iterations employed in the estimation. This class nests several estimators proposed in the literature such as those in Aguirregabiria and Mira (2002, 2007), Pesendorfer and Schmidt-Dengler (2008), and Pakes et al. (2007). First, we establish that the K-PML estimator is consistent and asymptotically normal for all K. This complements findings in Aguirregabiria and Mira (2007), who focus on K=1 and K large enough to induce convergence of the estimator. Furthermore, we show under certain conditions that the asymptotic variance of the K-PML estimator can exhibit arbitrary patterns as a function of K. Second, we establish that the K-MD estimator is consistent and asymptotically normal for all K. For a specific weight matrix, the K-MD estimator has the same asymptotic distribution as the K-PML estimator. Our main result provides an optimal sequence of weight matrices for the K-MD estimator and shows that the optimally weighted K-MD estimator has an asymptotic distribution that is invariant to K. The invariance result is especially unexpected given the findings in Aguirregabiria and Mira (2007) for K-PML estimators. Our main result implies two new corollaries about the optimal 1-MD estimator (derived by Pesendorfer and Schmidt-Dengler (2008)). First, the optimal 1-MD estimator is optimal in the class of K-MD estimators. In other words, additional policy iterations do not provide asymptotic efficiency gains relative to the optimal 1-MD estimator. Second, the optimal 1-MD estimator is more or equally asymptotically efficient than any K-PML estimator for all K. Finally, the appendix provides appropriate conditions under which the optimal 1-MD estimator is asymptotically efficient.
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PDF链接：
https://arxiv.org/pdf/1802.06665