PANEL STRUCTURAL MODELING WITH WEAK INSTRUMENTATION AND COVARIANCE RESTRICTIONS
弱工具变量与协方差约束下的面板结构建模AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究面板联立方程结构模型、弱工具变量及识别约束。
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弱工具变量与协方差约束下的面板结构建模
- 研究问题
- 当面板联立方程的系数识别约束或协方差约束失效、或仅弱成立时，如何兼顾估计一致性、精度与稳健性？
- 方法与思路
- 作者在横截面和时间维度同时增大的框架下比较多种估计量，并以 BIC 变换赋权组合 GMM 与偏差修正 GMM。
- 材料陈述的贡献
- 摘要称，文章提出两种模型平均估计量，对两类约束可能失效具有稳健性质，并分析弱工具下既有估计量的局限。
- 阅读关注点
- 应核对约束弱化的渐近设定、各估计量成立的条件、协方差错设导致的二阶偏差，以及 BIC 权重的具体形式。
- 阅读边界
- 仅据所给 Crossref 摘要，不能认定新估计量对任意约束失效均稳健；摘要未说明完整条件和比较数值，也不推断刊本变化。
原文摘要
This paper considers estimating a panel data simultaneous equations model under both coefficient and covariance matrix restrictions in a scenario where one or the other set of identifying restrictions may be invalid or may hold only weakly. We study the limiting properties of various estimators in an asymptotic framework, which takes both the cross-sectional dimension N and the time dimension T to infinity. In this setting as in the pure cross-sectional setup, the performance of the 2SLS estimator depends on the strength of the identifying conditions imposed on the coefficients of the model, and it fails to be consistent once these conditions break down sufficiently resulting in instruments that are too weakly correlated with the endogenous regressors. On the other hand, the between-group ( BG ) estimator is consistent and asymptotically normal even when coefficient restrictions fail, but it has the shortcoming that its precision depends only on variations in the cross-sectional dimension; and, hence, it is less efficient and has slower rate of convergence than alternatives, which make better use of the large time dimension. A GMM estimator, which combines the moment conditions of the BG estimator with that of the within-group IV estimator, is more robust to instrument weakness than 2SLS and is more efficient than the BG estimator, but it has a second-order bias even under strong instruments if the assumed covariance restrictions do not hold. To remedy the deficiency of the aforementioned estimators, we propose in this paper two new model averaging estimators, which are weighted averages of the GMM estimator and a bias-corrected GMM estimator. The two proposed estimators have weighting functions that depend on alternative transformations of the Bayesian Information Criterion (BIC), which is employed here to assess the validity of the covariance restrictions. We show that these new estimators have some nice robustness properties against possible failure of either the coefficient restrictions or the covariance restrictions.