PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS
PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS
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DOI：
10.3982/ecta6135
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发表时间：
2009-07-01
期刊：
影响因子：
6.1
通讯作者：
Bai, Jushan
中科院分区：
文献类型：
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作者：
Bai, Jushan
本文考虑具有不可观测多重交互效应的大N和大T面板数据模型，这些模型与回归量相关。例如，在收入研究中，除了通常认为的先天能力外，工人的动机、毅力和勤奋也会影响收入。在宏观经济学中，交互效应代表不可观察的共同冲击及其在横截面上的异质影响。我们考虑了交互效应估计量的辨识性、一致性和极限分布。在大N和大T下，估计量被证明是根NT一致的，这在两个维度中存在未知形式的相关性和异方差时是有效的。我们还推导了约束估计量及其极限分布，并施加了可加性和交互效应。本文还研究了加性效应与交互效应的测试问题。此外，我们还考虑了在存在大均值、时不变回归量和共同回归量的情况下模型的识别和估计。给定识别，收敛速度和极限结果继续成立。
This paper considers large N and large T panel data models with unobservable multiple interactive effects, which are correlated with the regressors. In earnings studies, for example, workers' motivation, persistence, and diligence combined to influence the earnings in addition to the usual argument of innate ability. In macroeconomics, interactive effects represent unobservable common shocks and their heterogeneous impacts on cross sections. We consider identification, consistency, and the limiting distribution of the interactive-effects estimator. Under both large N and large T, the estimator is shown to be root NT consistent, which is valid in the presence of correlations and heteroskedasticities of unknown form in both dimensions. We also derive the constrained estimator and its limiting distribution, imposing additivity coupled with interactive effects. The problem of testing additive versus interactive effects is also studied. In addition, we consider identification and estimation of models in the presence of a grand mean, time-invariant regressors, and common regressors. Given identification, the rate of convergence and limiting results continue to hold.