Topics in Dynamic Panel Data Analysis, Time-Varying Individual Heterogeneities, and Cross-Sectional Dependence

动态面板数据分析、时变个体异质性和横截面依赖性主题

• 批准号: 0962410
• 负责人: Jushan Bai
• 金额: $ 22.7万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0962410&HistoricalAwards=false
• 关键词: Topics; Dynamic; Panel; Data; Analysis

This research deals with estimation and inference problems for dynamic panel-data models under time-varying individual heterogeneities and cross-sectional dependence (common shocks). An important aspect of these problems is that the individual heterogeneity and the common shocks are correlated with the explanatory variables. This correlation is fundamental for economic variables. Standard procedures such as within-group estimators will lead to inconsistent inferences. This research explores new estimation procedures and related inference problems. It also presents feasible implementation of the suggested procedures.The last two decades have witnessed a huge development of panel data econometrics, as panel data techniques can solve issues that are hard to solve by either the cross section or time series procedures alone. With the increasing availability of panel data sets, the associated techniques have become the key tools of empirical researchers. The recent advancement and the importance of the panel techniques are summarized by three excellent monographs: Arellano (2003), Batagi (2006), and Hsiao (2003). Much of this literature has focused on the case of time-invariant individual heterogeneities.Intellectual merit: The research considers models that allow the individual effects to be time varying, and the time effects (or common shocks) to have different impacts across individuals. Such models have both empirical and theoretical foundations, as detailed in the projection escription section. Moreover, the individual heterogeneities and the common shocks are allowed to be correlated with the regressors. This correlation arises naturally for economic variables when choice and decisions are involved. In this project, the PI will consider how to formulate the problem so that the estimation can be handled by the traditional methods such as the nonlinear generalized least squares or the quasi-maximum likelihood method. Careful analysis for small T (time periods) dynamic panel models will be rendered. Panel unit root and panel cointegration problems under both fixed T and large T will be considered. The corresponding inferential theory will be derived. Furthermore, models with heterogeneous slope coeffcients, their estimation, and inference will be studied. As in Alvarez and Arrelano (2005), robust likelihood that allows for changing variance will be considered, as the changing variance itself may be the object of interest. All the analysis will be conducted in the presence of time-varying heterogeneities and in the presence of correlation between the effects and regressors. This research will advance our knowledge and understanding of panel data models; it will enrich panel data analysis and result in additional tools for empirical studies. Broader impact: This research deals with new methodologies and their implementations. Within economics, the methods are applicable in labor economics, industrial organization, and macroeconomics. These methods are also applicable outside the field of economics when panel data methods are called for. Computer programs will be made available to the general public. The proposed research will also enrich classroom teachings. NSF funding will help train graduate students for theoretical and computational work.

本研究探讨动态面板资料模型在时变个体异质性与横截面相依（共同冲击）下的估计与推论问题。这些问题的一个重要方面是个体异质性和共同冲击与解释变量相关。这种相关性对经济变量来说是基本的。标准程序，如组内估计量，将导致不一致的推论。 本研究探讨新的估计程序和相关的推理问题。在过去的20年里，面板数据计量经济学得到了巨大的发展，因为面板数据技术可以解决单独使用横截面或时间序列方法难以解决的问题。随着面板数据的可获得性越来越高，相关技术已成为实证研究者的重要工具。Arellano（2003）、Batagi（2006）和Hsiao（2003）这三本优秀的专著总结了面板技术的最新进展和重要性。大部分的文献都集中在时间不变的个人heterogeneitys.Intellectual优点的情况下：研究认为，模型，允许个人的影响是随时间变化的，和时间的影响（或共同的冲击），有不同的个人的影响。这些模型既有经验基础，也有理论基础，详见预测描述部分。此外，个别的异质性和共同的冲击，允许与回归。当涉及选择和决策时，这种相关性自然会出现在经济变量中。 在这个项目中，PI将考虑如何制定的问题，使估计可以处理的传统方法，如非线性广义最小二乘法或准最大似然法。对小T（时间段）动态面板模型进行了细致的分析。固定T和大T下的面板单位根和面板协整问题将被考虑。相应的推理理论将被导出。此外，还将研究具有非均质坡度系数的模型、其估计和推理。与Alvarez和Arrelano（2005）一样，将考虑允许变化方差的鲁棒似然性，因为变化方差本身可能是感兴趣的对象。所有分析将在存在时变异质性以及效应和回归变量之间存在相关性的情况下进行。 这项研究将促进我们的知识和理解的面板数据模型，它将丰富面板数据分析，并导致额外的工具，实证研究。更广泛的影响：这项研究涉及新的方法及其实施。在经济学中，这些方法适用于劳动经济学，产业组织和宏观经济学。这些方法也适用于经济学以外的领域，当面板数据方法的要求。 计算机程序将向公众提供。建议的研究也将丰富课堂教学。 NSF的资金将帮助培养研究生的理论和计算工作。