Semiparametric estimation of the intercept of a sample selection model
Semiparametric estimation of the intercept of a sample selection model
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DOI：
10.1111/1467-937x.00055
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发表时间：
1998-07-01
影响因子：
5.8
通讯作者：
Schafgans, MMA
中科院分区：
文献类型：
--
作者：
Andrews, DWK;Schafgans, MMA
本文给出了半参数估计样本选择模型截距的相合渐近正态估计。当样本量趋于无穷大时，估计量使用的是所有观测值中逐渐减少的一小部分，如Heckman（1990）。在半参数文献中，截距的估计通常包含在非参数样本选择偏倚校正项中。然而，从经济角度来看，截距的估计是重要的。例如，它使人们能够确定加入工会和不加入工会的工人之间的“工资差距”，分解不同社会经济群体（例如男女和黑人白人）之间的工资差距，并评估社会方案的净效益。
This paper provides a consistent and asymptotically normal estimator for the intercept of a semiparametrically estimated sample selection model. The estimator uses a decreasingly small fraction of all observations as the sample size goes to infinity, as in Heckman (1990). In the semiparametrics literature, estimation of the intercept has typically been subsumed in the nonparametric sample selection bias correction term. The estimation of the intercept, however, is important from an economic perspective. For instance, it permits one to determine the "wage gap" between unionized and nonunionized workers, decompose the wage differential between different socioeconomic groups (e.g. male-female and black-white), and evaluate the net benefits of a social programme.