Inference in panel data models under attrition caused by unobservables
Inference in panel data models under attrition caused by unobservables
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DOI：
10.1016/j.jeconom.2008.03.002
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发表时间：
2008-06-01
影响因子：
6.3
通讯作者：
Bhattacharya, Debopam
中科院分区：
文献类型：
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作者：
Bhattacharya, Debopam
本文研究了在不可重复的样本损耗下，面板数据模型中有限维参数的识别与估计。衰减可以取决于第二周期变量，该第二周期变量对于衰减器是不可观测的，但是来自第二周期值的边缘分布的独立刷新样本是可用的。本文表明，在准可分性假设下，该模型隐含着一组条件矩的限制，其中的时刻包含的摩擦功能作为一个未知参数。该公式导致（i）在严格弱于现有文献的条件下的简单识别证明，更重要的是，（ii）有限维参数的基于筛的根-n一致估计。这些方法适用于具有内生性损耗的线性和非线性面板数据模型，类似的方法适用于单个横截面中存在内生性缺失数据的情况。该理论是说明了一个模拟练习，使用当前的人口调查数据的面板结构引入的旋转组功能的抽样过程。(C)2008 Elsevier B. V.保留所有权利。
This paper concerns identification and estimation of a finite-dimensional parameter in a panel data-model under nonignorable sample attrition. Attrition can depend on second period variables which are unobserved for the attritors but an independent refreshment sample from the marginal distribution of the second period values is available. This paper shows that under a quasi-separability assumption, the model implies a set of conditional moment restrictions where the moments contain the attrition function as an unknown parameter. This formulation leads to (i) a simple proof of identification under strictly weaker conditions than those in the existing literature and, more importantly, (ii) a sieve-based root-n consistent estimate of the finite-dimensional parameter of interest. These methods are applicable to both linear and nonlinear panel data models with endogenous attrition and analogous methods are applicable to situations of endogenously missing data in a single cross-section. The theory is illustrated with a simulation exercise, using Current Population Survey data where a panel structure is introduced by the rotation group feature of the sampling process. (C) 2008 Elsevier B.V. All rights reserved.