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分类依据与纠错

AI 根据所提供的论文材料归类:方法直接针对动态面板的截面相关,且应用于 OECD 国家增长面板,因此同时对应面板数据与宏观面板。

反馈分类问题

A heteroskedasticity robust Breusch–Pagan test for Contemporaneous correlation in dynamic panel data models

动态面板同期相关的异方差稳健 Breusch–Pagan 检验

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
Andreea G. Halunga
Chris D. Orme
Takashi Yamagata

动态面板同期相关的异方差稳健 Breusch–Pagan 检验

研究问题
在线性动态面板中,存在异方差、非正态误差且不必假定截面独立时,如何检验截面之间的同期零相关?
方法与思路
作者提出异方差稳健 BP 检验,分析固定 N 或 N²/T 趋零时的渐近有效性,并设计多种 wild bootstrap 校准。
材料陈述的贡献
摘要称 T 相对 N 较大时渐近近似较好;T/N 较小时 bootstrap 改善尺寸控制,递归设计方案在模拟中通常表现最好。
阅读关注点
应核对零相关与截面独立的区别、各类解释变量条件,以及不同 bootstrap 如何保留动态结构和截面误差关系。
阅读边界
所给出版社摘要明确限制了渐近增长路径;T 与 N 接近时的改善来自所报告模拟,不能据此保证任意小样本都可靠。

原文摘要

This paper proposes a heteroskedasticity-robust Breusch–Pagan test of the null hypothesis of zero cross-section (or contemporaneous) correlation in linear panel data models, without necessarily assuming independence of the cross-sections. The procedure allows for either fixed, strictly exogenous and/or lagged dependent regressor variables, as well as quite general forms of both non-normality and heteroskedasticity in the error distribution. The asymptotic validity of the test procedure is predicated on the number of time series observations, T, being large relative to the number of cross-section units, N, in that: either (i) N is fixed as T→∞; or, (ii) N2/T→0 as both T and N diverge, jointly, to infinity. Given this, it is not expected that asymptotic theory would necessarily provide an adequate guide to finite sample performance when T/N is “small”. Because of this we also propose, and establish asymptotic validity of, a number of wild bootstrap schemes designed to provide improved inference when T/N is small. Across a variety of experimental designs, a Monte Carlo study suggests that the predictions from asymptotic theory do, in fact, provide a good guide to the finite sample behaviour of the test when T is large relative to N. However, when T and N are of similar orders of magnitude, discrepancies between the nominal and empirical significance levels occur as predicted by the first-order asymptotic analysis. On the other hand, for all the experimental designs, the proposed wild bootstrap approximations do improve agreement between nominal and empirical significance levels, when T/N is small, with a recursive-design wild bootstrap scheme performing best, in general, and providing quite close agreement between the nominal and empirical significance levels of the test even when T and N are of similar size. Moreover, in comparison with the wild bootstrap “version” of the original Breusch–Pagan test (Godfrey and Yamagata, 2011) our experiments indicate that the corresponding version of the heteroskedasticity-robust Breusch–Pagan test appears reliable. As an illustration, the proposed tests are applied to a dynamic growth model for a panel of 20 OECD countries.

笔记与分组