Unobserved Heterogeneity in the Binary Logit Model with Cross-Sectional Data and Short Panels: A Finite Mixture Approach
Unobserved Heterogeneity in the Binary Logit Model with Cross-Sectional Data and Short Panels: A Finite Mixture Approach
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具有横截面数据和短面板的二元 Logit 模型中未观察到的异质性：有限混合方法
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
M. Pedersen
中科院分区：
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作者：
Anders Holm;M. Jæger;M. Pedersen
本文提出了一种利用截面数据和短面板的二元Logit模型来处理应用研究中未观察到的异质性的新方法。在诸如二元Logit模型的非线性回归模型中，未观察到的异质性尤其重要，因为与线性回归模型不同，即使忽略的自变量与观察到的自变量不相关，对观察到的自变量的影响的估计也是有偏差的。我们提出了一个基于有限混合方法的二元Logit模型的扩展，在该模型中，我们通过潜在类来概念化未观察到的异质性。仿真结果表明，与标准的Logit模型相比，我们的方法在估计自变量的影响时产生的偏差要小得多。此外，由于当研究人员有横截面数据而不是面板数据时，对未观察到的异质性的识别很弱，我们提出了一种简单的方法，固定潜在类权重，改进识别和估计。最后，我们使用加拿大关于公众支持再分配的调查数据来说明我们的新方法的适用性。
This paper proposes a new approach to dealing with unobserved heterogeneity in applied research using the binary logit model with cross-sectional data and short panels. Unobserved heterogeneity is particularly important in non-linear regression models such as the binary logit model because, unlike in linear regression models, estimates of the effects of observed independent variables are biased even when omitted independent variables are uncorrelated with the observed independent variables. We propose an extension of the binary logit model based on a finite mixture approach in which we conceptualize the unobserved heterogeneity via latent classes. Simulation results show that our approach leads to considerably less bias in the estimated effects of the independent variables than the standard logit model. Furthermore, because identification of the unobserved heterogeneity is weak when the researcher has cross-sectional rather than panel data, we propose a simple approach that fixes latent class weights and improves identification and estimation. Finally, we illustrate the applicability of our new approach using Canadian survey data on public support for redistribution.