The paper compares different estimation strategies of ordered response models in the presence of non-random unobserved heterogeneity. By running Monte Carlo simulations with a range of randomly generated panel data of differing cross-sectional and longitudinal dimension sizes, we assess the consistency and efficiency of standard models such as linear fixed effects, ordered and conditional logit, and several different binary recoding procedures. Among the binary recoding procedures analyzed are the conditional ordered logit estimator proposed by Ferrer-i-Carbonell and Frijters (2004) that recently has gained some popularity in the analysis of individual well-being, as well as the new developed "Blow-Up and Cluster" (BUC) estimator of Baetschmann et al. (2011). The Ferrer-i-Carbonell and Frijters estimator (FCF) performs best if the number of observations is large and the number of categories on the ordered scale is small. However, the BUC method performs similarly well and even outperforms the FCF estimator if the number of categories on the ordered scale is large. If the researcher is only interested in the relative size of coefficients with respect to a baseline, however, the easy-to-compute linear fixed effects model delivers essentially the same results as the more elaborate binary recoding schemes.
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机译：本文比较了存在非随机未观察到的异质性时有序响应模型的不同估计策略。通过对一系列具有不同横截面和纵向尺寸大小的随机生成的面板数据进行蒙特卡洛模拟，我们评估了标准模型（如线性固定效应，有序和条件对数）以及几种不同的二进制编码程序的一致性和效率。在分析的二进制编码程序中，有Ferrer-i-Carbonell和Frijters（2004）提出的条件有序logit估计器，该方法最近在分析个人福祉方面颇受欢迎，以及新开发的“ Blow-up and Baetschmann等人的“簇”（BUC）估计量。 （2011）。如果观察的数量很多并且有序规模的类别数量很小，则Ferrer-i-Carbonell和Frijters估计器（FCF）的效果最佳。但是，如果有序规模上的类别数量很大，则BUC方法的性能相似，甚至优于FCF估计器。但是，如果研究人员只对相对于基线的系数的相对大小感兴趣，那么易于计算的线性固定效应模型所提供的结果与更复杂的二进制编码方案基本相同。
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