Short T dynamic panel data models with individual, time and interactive effects
含个体、时间及交互效应的短期动态面板模型AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究短期动态面板，并分别应用于跨县犯罪率和跨国增长回归。
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含个体、时间及交互效应的短期动态面板模型
- 研究问题
- 在时间维度较短、同时存在个体效应、时间效应和多因子交互效应的动态面板中，如何识别并估计模型参数？
- 方法与思路
- 作者提出变换准最大似然估计，以多因子误差描述交互效应，并用序贯多重似然比检验估计因子数量。
- 材料陈述的贡献
- 摘要称，估计量具有一致性和渐近正态性，因子数选择程序一致；同时指出含滞后因变量时不能保证全局识别。
- 阅读关注点
- 应核对因子数识别的阶数条件、参数局部识别条件、短面板的渐近框架，以及初值异质性和单位根情形的处理。
- 阅读边界
- 仅据所给 Crossref 摘要，无法核验定理条件和模拟设定；多数设定下表现良好不能外推为所有短面板均适用。
原文摘要
Summary This paper proposes a transformed quasi‐maximum likelihood (TQML) estimator for short dynamic fixed effects panel data models allowing for interactive effects through a multifactor error structure. The proposed estimator is robust to the heterogeneity of the initial values and common unobserved effects, while at the same time allowing for standard fixed and time effects. It is applicable to both stationary and unit root cases. The order condition for identification of the number of interactive effects is established, and conditions are derived under which the parameters are locally identified. It is shown that global identification in the presence of the lagged dependent variable cannot be guaranteed. The TQML estimator is proven to be consistent and asymptotically normally distributed. A sequential multiple testing likelihood ratio procedure is also proposed for estimation of the number of factors which is shown to be consistent. Finite sample results obtained from Monte Carlo simulations show that the proposed procedure for determining the number of factors performs very well, and the TQML estimator has small bias and root mean square error (RMSE) and correct empirical size in most settings. The practical use of the TQML approach is demonstrated by means of two empirical illustrations from the literature on cross county crime rates and cross country growth regressions.