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.

这项研究涉及在随时间变化的个体异质性和横截面依赖性（常见冲击）下的动态面板数据模型的估计和推理问题。这些问题的一个重要方面是，个体的异质性和共同冲击与解释变量相关。这种相关性对于经济变量至关重要。诸如组内估计器之类的标准程序将导致不一致的推论。 这项研究探讨了新的估计程序和相关的推断问题。它还提出了建议的程序的可行实施。最近二十年来，面板数据计量经济学的发展很大，因为面板数据技术可以解决仅横截面或时间序列程序就难以解决的问题。随着面板数据集的可用性的增加，相关技术已成为经验研究人员的关键工具。三个出色的专着：Arellano（2003），Batagi（2006）和Hsiao（2003）总结了面板技术的最新进步和重要性。这些文献的大部分都集中在时间不变的个体异质性的情况下。智能优点：研究考虑了允许单个效应变化的模型，时间效应（或常见的冲击）在个人之间产生不同的影响。这种模型具有经验和理论基础，如投影表述部分所述。此外，允许单个异质性和常见的冲击与回归器相关。当涉及选择和决策时，这种相关性自然而归。 在该项目中，PI将考虑如何提出问题，以便可以通过传统方法（例如非线性通用最小二乘正方形或准最大最大可能性方法）来处理估计。将对小t（时间段）动态面​​板模型进行仔细分析。将考虑固定T和大T下的面板单位根和面板协整问题。相应的推论理论将得出。此外，将研究具有异质斜率系数，其估计和推理的模型。就像Alvarez和Arrelano（2005）中一样，将考虑允许变化的强大可能性，因为不断变化的方差本身可能是感兴趣的对象。所有分析将在存在时间变化的异质性的情况下以及效应与回归器之间存在相关性的情况下进行。 这项研究将提高我们对面板数据模型的知识和理解；它将丰富面板数据分析，并为实证研究提供其他工具。更广泛的影响：这项研究涉及新方法及其实施。在经济学中，这些方法适用于劳动经济学，工业组织和宏观经济学。当需要面板数据方法时，这些方法也适用于经济学领域。 计算机程序将提供给公众。拟议的研究还将丰富课堂教学。 NSF资金将帮助培训研究生的理论和计算工作。