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本文发展了一个针对样本选择模型的两阶段半参数估计量,其首先在第一阶段基于对数欧几里得分布差异测度估计离散选择概率,进而在第二阶段利用非参数sieve方法估计一个包含参数和非参数部分的部分线性模型以得到模型参数的估计。相对于文献中已有的半参数估计量,该估计量的计算更加简便,且计算负担相对较小。我们说明了该半参数估计量的一致性和渐近正态性,同时给出了其渐近方差的计算公式。蒙特卡洛模拟结果符合我们的理论结论。
Abstract：This paper proposes a two-step semiparametric estimator for sample selection models,which estimates the choice probabilities based on the Log-Euclidean distribution divergence measure in the first step,and estimate a partial linear model which contains parametric and nonparametric parts using nonparametric sieve method in the second step.Comparing with the existing semiparametric estimators,the implementation is more convenient and the computational burden is smaller of the estimator proposed in this paper.We demonstrate the consistency and asymptotic normality of our estimator,and derive the formula of the asymptotic variance.Monte Carlo results are consistent with the theoretical conclusion.
②Heckman两步法实际上需要的假设要比联合正态假定(5)弱,参见Olsen(1980)以及Cameron和Trivedi(2005)。
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基本信息:
DOI：10.19343/j.cnki.11-1302/c.2012.02.012
中图分类号:O212.1
引用信息:
[1]王亚峰.样本选择模型的一个简单半参数估计量[J].统计研究,2012,29(02):88-93.DOI:10.19343/j.cnki.11-1302/c.2012.02.012.
2012-02-15
2012-02-15