Finite‐sample comparison of alternative methods for estimating dynamic panel data models
动态面板数据模型不同估计方法的有限样本比较AI 解读 ↓
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动态面板数据模型不同估计方法的有限样本比较
- 研究问题
- 在非线性动态随机效应面板中，处理初始值问题的 Wooldridge 方法，其有限样本表现与稳健性如何随面板长度变化？
- 方法与思路
- 作者以动态随机效应 probit 和Ⅰ型 Tobit 为例，通过蒙特卡洛实验及两个实证案例，比较四种初始值处理方案。
- 材料陈述的贡献
- 摘要称 Wooldridge 方法在超过约5—8期的面板中表现较好，短于5期时建议 Heckman 近似，较长面板的方法表现趋近。
- 阅读关注点
- 应核对初始值生成方式、随机效应设定、各方法的比较指标，以及所报面板长度经验界限是否随实验设计改变。
- 阅读边界
- 仅据所给 Crossref 摘要，无法判断全部实验条件与稳健性范围；所报期数界限不能直接视为普遍适用的选法规则。
原文摘要
SUMMARY The Wooldridge method is based on a simple and novel strategy to deal with the initial values problem in nonlinear dynamic random‐effects panel data models. The characteristic of the method makes it very attractive in empirical applications. However, its finite sample performance and robustness are not fully known as of yet. In this paper we investigate the performance and robustness of this method in comparison with an ideal case in which the initial values are known constants; the worst scenario is based on an exogenous initial values assumption, and the Heckman's reduced‐form approximation method, which is widely used in the literature. The dynamic random‐effects probit and Tobit (type I) models are used as working examples. Various designs of the Monte Carlo experiments and two further empirical illustrations are provided. The results suggest that the Wooldridge method works very well only for the panels of moderately long duration (longer than 5–8 periods). Heckman's reduced‐form approximation is suggested for short panels (shorter than 5 periods). It is also found that all the methods tend to perform equally well for panels of long duration (longer than 15–20 periods). Copyright © 2011 John Wiley & Sons, Ltd.