Bootstrap inference for panel data models wirh serial and cross sectional dependence of unknown form

具有未知形式的序列和横截面依赖性的面板数据模型的引导推理

• 批准号: 341322-2009
• 负责人: Gonçalves, Silvia
• 金额: $ 1.17万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=482052
• 关键词: Bootstrap; inference; panel; data; models

The first part of this program considers bootstrap methods for panel data regression models estimated with fixed effects. In particular, I develop a new bootstrap method for panel regression models that is robust to serial and cross sectional dependence of unknown form in the error term. I call it the panel block bootstrap method because it resamples blocks of the vector that collects the cross section units for each time period. It is the bootstrap analogue of the Driskoll and Kraay (1998) covariance estimator for spatial and serial dependent panel data models. Preliminary simulations show that the panel block bootstrap has good finite sample properties and dominates the Driskoll and Kraay (1998) robust standard errors. I prove the first order asymptotic validity of the panel block bootstrap method under conditions similar to those used by Driskoll and Kraay (1998). Then I investigate to what extent the panel block bootstrap can outperform the asymptotic theory of Driskoll and Kraay (1998).

本程序的第一部分考虑使用固定效应估计的面板数据回归模型的引导方法。特别是，我为面板回归模型开发了一种新的引导方法，该方法对于误差项中未知形式的序列和横截面依赖性具有鲁棒性。我将其称为面板块引导方法，因为它对收集每个时间段的横截面单位的向量块进行重新采样。它是空间和序列相关面板数据模型的 Driskoll 和 Kraay (1998) 协方差估计器的引导模拟。初步模拟表明，面板块引导程序具有良好的有限样本属性，并且在 Driskoll 和 Kraay (1998) 稳健标准误差中占主导地位。我在类似于 Driskoll 和 Kraay (1998) 使用的条件下证明了面板块引导方法的一阶渐近有效性。然后我研究了面板块引导在多大程度上可以优于 Driskoll 和 Kraay (1998) 的渐近理论。