Estimation and Inference in High-Dimensional Panel Data Models

高维面板数据模型中的估计和推理

• 批准号: 501082519
• 负责人: Professor Dr. Michael Vogt
• 金额: --
• 依托单位: Institut für Statistik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/501082519?language=en
• 关键词: Estimation; Inference; Dimensional; Panel; Data

The analysis of many modern economic panel data sets requires to account for both unobserved heterogeneity and high-dimensional data structures. Nevertheless, only few econometric methods have been developed to analyze high-dimensional panel data models with unobserved heterogeneity. The main purpose of the project is to devise novel estimation and inference techniques for such models. We will focus attention on models with interactive fixed effects, which are a very flexible and popular framework to take into account unobserved heterogeneity. A very common way to estimate the unknown parameters in a low-dimensional panel model with interactive fixed effects is the so-called common correlated effects (CCE) estimator introduced by Pesaran in 2006. However, this very popular estimator breaks down in high dimensions, and naive extensions to the high-dimensional case fail dramatically. In the project, we will develop a novel CCE-type estimator which does work in high dimensions. The theoretical part of the project will be concerned with deriving the asymptotic properties of the proposed estimator. In a first step, we will concentrate on estimation theory and derive the convergence rate of the estimator. In a second step, we will turn to distribution theory and analyze inferential procedures based on it. The methodological and theoretical analysis of the project will be complemented by simulation studies and empirical applications.

对许多现代经济面板数据集的分析要求同时考虑未观察到的异质性和高维数据结构。然而，只有很少的计量经济学方法被用来分析高维面板数据模型，这些模型具有未被观察到的异质性。该项目的主要目的是为这些模型设计新的估计和推理技术。我们将重点关注具有交互固定效果的模型，这是一个非常灵活和流行的框架，可以考虑到未观察到的异质性。在具有交互固定效应的低维面板模型中，估计未知参数的一种非常常见的方法是由Pesaran于2006年提出的所谓的共同相关效应(CCE)估计器。然而，这一非常流行的估计器在高维情况下被分解，而对高维情况的天真扩展显著失败。在这个项目中，我们将开发一种新型的CCE型估计器，它确实可以在高维情况下工作。该项目的理论部分将涉及到推导所提出的估计器的渐近性质。在第一步中，我们将集中于估计理论，并推导出估计量的收敛速度。在第二步中，我们将转向分布理论并分析基于它的推理过程。该项目的方法和理论分析将得到模拟研究和经验应用的补充。