Many applied settings in empirical economics require estimation of a large number of individual effects, like teacher effects or location effects; in health economics, prominent examples include patient effects, doctor effects or hospital effects. Increasingly, these effects are the object of interest of the estimation, and predicted effects are often used for further descriptive and regression analyses. To avoid imposing distributional assumptions on these effects, they are typically estimated via fixed effects methods. In short panels, the conventional maximum likelihood estimator for fixed effects binary response models provides poor estimates of these individual effects since the finite sample bias is typically substantial. We present a bias-reduced fixed effects estimator that provides better estimates of the individual effects in these models by removing the first-order asymptotic bias. An additional, practical advantage of the estimator is that it provides finite predictions for all individual effects in the sample, including those for which the corresponding dependent variable has identical outcomes in all time periods over time (either all zeros or ones); for these, the maximum likelihood prediction is infinite. We illustrate the approach in simulation experiments and in an application to health care utilization.

中文翻译：

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在固定效应面板概率模型中预测个体效应

实证经济学中的许多应用环境需要估计大量个体效应，例如教师效应或位置效应；在卫生经济学中，突出的例子包括患者效应、医生效应或医院效应。这些影响越来越成为估计的关注对象，并且预测的影响通常用于进一步的描述和回归分析。为了避免对这些影响强加分布假设，它们通常通过固定效应方法进行估计。在短面板中，固定效应二元响应模型的传统最大似然估计量对这些单个效应的估计很差，因为有限样本偏差通常很大。我们提出了一个减少偏差的固定效应估计器，它通过消除一阶渐近偏差来更好地估计这些模型中的单个效应。估计量的另一个实用优势是，它为样本中的所有单个效应提供有限预测，包括相应因变量在所有时间段内具有相同结果（全为零或全为 1）的那些；对于这些，最大似然预测是无限的。我们在模拟实验和医疗保健应用中说明了该方法。包括那些对应因变量在所有时间段内具有相同结果的变量（全为零或全为 1）；对于这些，最大似然预测是无限的。我们在模拟实验和医疗保健应用中说明了该方法。包括那些对应因变量在所有时间段内具有相同结果的变量（全为零或全为 1）；对于这些，最大似然预测是无限的。我们在模拟实验和医疗保健应用中说明了该方法。