论文标题
将潜在组结构的先验知识纳入面板数据模型
Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models
论文作者
论文摘要
小组异质性的假设在面板数据模型中已流行。我们开发了一个受约束的贝叶斯分组估计器，该估计量以成对约束的形式利用研究人员对组的先前信念，表明一对单元是否可能属于同一组或不同的组。我们在以不同程度的置信度合并之前提出了一个成对约束。整个框架建立在非参数贝叶斯方法上，该方法隐含地指定了组分区的分布，因此后验分析将潜在组结构的不确定性考虑在内。蒙特卡洛实验表明，添加先验知识会产生更准确的系数估计值，而得分比替代估计量获得了预测提高。我们将方法应用于两个经验应用。在预测美国CPI通货膨胀的第一个应用程序中，我们说明，当数据不完全提供信息时，群体的先验知识会改善密度预测。第二次申请重新审视了一个国家的收入与其民主过渡之间的关系；我们确定了对民主的异构收入影响，五十个国家 /地区有五个截然不同的群体。
The assumption of group heterogeneity has become popular in panel data models. We develop a constrained Bayesian grouped estimator that exploits researchers' prior beliefs on groups in a form of pairwise constraints, indicating whether a pair of units is likely to belong to a same group or different groups. We propose a prior to incorporate the pairwise constraints with varying degrees of confidence. The whole framework is built on the nonparametric Bayesian method, which implicitly specifies a distribution over the group partitions, and so the posterior analysis takes the uncertainty of the latent group structure into account. Monte Carlo experiments reveal that adding prior knowledge yields more accurate estimates of coefficient and scores predictive gains over alternative estimators. We apply our method to two empirical applications. In a first application to forecasting U.S. CPI inflation, we illustrate that prior knowledge of groups improves density forecasts when the data is not entirely informative. A second application revisits the relationship between a country's income and its democratic transition; we identify heterogeneous income effects on democracy with five distinct groups over ninety countries.
