A COMPUTATIONALLY PRACTICAL SIMULATION ESTIMATION ALGORITHM FOR DYNAMIC PANEL DATA MODELS WITH UNOBSERVED ENDOGENOUS STATE VARIABLES*
A COMPUTATIONALLY PRACTICAL SIMULATION ESTIMATION ALGORITHM FOR DYNAMIC PANEL DATA MODELS WITH UNOBSERVED ENDOGENOUS STATE VARIABLES*
复制标题
具有未观测到的内生状态变量的动态面板数据模型的计算实用模拟估计算法*
DOI：
10.1111/j.1468-2354.2010.00606.x
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发表时间：
2010
影响因子：
1.5
通讯作者：
Keane M
中科院分区：
文献类型：
--
作者：
Keane M
本文开发了一种模拟估计算法，该算法对于估计具有未观察到的内生状态变量的动态面板数据模型特别有用。对具有序列相关误差的动态概率模型的重复采样实验表明，估计器具有良好的小样本特性。我们将该估计量应用于女性劳动力供给模型，结果表明很少使用的 Polya 模型比流行的马尔可夫模型更适合数据。波利亚模型还产生了更少的国家依赖性和更少的种族效应，以及教育、幼儿和丈夫收入对女性劳动力供应决策的更强的影响。
This article develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate the estimator has good small sample properties. We apply the estimator to a model of female labor supply and show that the rarely used Polya model fits the data substantially better than the popular Markov model. The Polya model also produces far less state dependence and many fewer race effects and much stronger effects of education, young children, and husband's income on female labor supply decisions.