论文标题：Efficient estimation of heterogeneous efficients in panel data models with common shocks

　　发表时间：2020

　　论文所有作者：Kunpeng Li,Guowei Cui, Lina Lu

　　期刊名及所属分类：Journal of Econometrics(国际A)

　　英文摘要：This paper investigates the estimation and inference issues of heterogeneous coefficients in panel data models with common shocks. We propose a novel two-step method to estimate the heterogeneous coefficients. We establish the asymptotic theory of our estimators, including consistency, asymptotic representation, and limiting distribution. Our two-step method can effectively address the limitations of the existing methods, such as the common correlated effects method proposed by Pesaran (2006) and the iterated principal components method proposed by Song (2013). The two-step estimator is as efficient as the two existing competitors in the basic model, and more efficient in the model with zero restrictions. Intensive Monte Carlo simulations show that the proposed estimator performs robustly in a variety of data setups.

　　中文摘要：本文研究了具有共同冲击的面板数据模型中非均质系数的估计和推断问题。我们提出了一种新的两步法来估计非均质系数。我们建立了估计量的渐近理论，包括相合性、渐近表示和极限分布。我们的两步法可以有效地解决现有方法的局限性，如Pesaran(2006)提出的常见相关效应法和Song(2013)提出的迭代主成分法。在基本模型中，两步估计与现有的两个竞争对手一样有效，而在无限制的模型中，两步估计的效率更高。密集的蒙特卡罗模拟表明，所提出的估计器在各种数据设置下都能稳健地执行。