Variance components structures for the extreme-value and logistic distributions with application to models of heterogeneity
Variance components structures for the extreme-value and logistic distributions with application to models of heterogeneity
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DOI：
10.1017/s0266466600005727
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发表时间：
1997-04-01
影响因子：
0.8
通讯作者：
Cardell, NS
中科院分区：
文献类型：
--
作者：
Cardell, NS
引入了两类新的概率分布，从根本上简化了开发极值和逻辑分布的方差分量结构的过程。当将这些新变量之一添加到极值（逻辑）变量时，所得分布也是极值（逻辑）分布。因此，通过递归地添加具有这种新分布的分量，可以生成相当复杂的方差结构，并且结果将保留边际极值（逻辑）分布。结果表明，极值误差结构的计算简单性扩展到引入持续时间、选择偏差、有限因变量和定性变量模型中的异质性。这些新分布类别的有用性通过嵌套合法模型、多元风险模型和竞争风险模型的示例进行了说明，其中开发了对传统随机结构的重要概括。新模型被证明比通过模拟矩估计等替代方案计算更简单且更容易处理。这些结果对于应用微观经济研究人员来说将具有相当大的用途，他们在构建更复杂的估计量时因计算困难而受到阻碍。
Two new classes of probability distributions are introduced that radically simplify the process of developing variance components structures for extreme-value and logistic distributions, When one of these new variates is added to an extreme-value (logistic) variate, the resulting distribution is also extreme value (logistic). Thus, quite complicated variance structures can be generated by recursively adding components having this new distribution, and the result will retain a marginal extreme-value (logistic) distribution. It is demonstrated that the computational simplicity of extreme-value error structures extends to the introduction of heterogeneity in duration, selection bias, limited-dependent- and qualitative-variable models. The usefulness of these new classes of distributions is illustrated with the examples of nested legit, multivariate risk, and competing risk models, where important generalizations to conventional stochastic structures are developed. The new models are shown to be computationally simpler and far more tractable than alternatives such as estimation by simulated moments. These results will be of considerable use to applied microeconomic researchers who have been hampered by computational difficulties in constructing more sophisticated estimators.