Random coefficient models for time-series-cross-section data: Monte Carlo experiments
Random coefficient models for time-series-cross-section data: Monte Carlo experiments
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DOI：
10.1093/pan/mpl001
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发表时间：
2007-03-01
影响因子：
5.4
通讯作者：
Katz, Jonathan N.
中科院分区：
文献类型：
--
作者：
Beck, Nathaniel;Katz, Jonathan N.
本文考虑时间序列横截面数据的随机系数模型（RCM）。这些模型允许模型参数中的单元到单元变化。文章的核心比较了完全合并估计量、逐个单位（未合并）估计量和（最大似然）RCM估计量的有限样本性质。最大似然估计器RCM表现良好，即使在数据生成使得RCM会有问题的情况下。在附录中，我们证明了RCM模型最常见的可行广义最小二乘估计量总是劣于最大似然估计量，并且在较小的样本中显着如此。
This article considers random coefficient models (RCMs) for time-series-cross-section data. These models allow for unit to unit variation in the model parameters. The heart of the article compares the finite sample properties of the fully pooled estimator, the unit by unit (unpooled) estimator, and the (maximum likelihood) RCM estimator. The maximum likelihood estimator RCM performs well, even where the data were generated so that the RCM would be problematic. In an appendix, we show that the most common feasible generalized least squares estimator of the RCM models is always inferior to the maximum likelihood estimator, and in smaller samples dramatically so.