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A note on transformed likelihood approach in linear dynamic panel models

机译：关于线性动态面板模型中的变换似然法的注释

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摘要

The transformed likelihood approach to estimation of fixed effects dynamic panel data models is shown to present very good inferential properties but it is not directly implemented in the most diffused statistical software. The present paper aims at showing how a simple model reformulation can be adopted to describe the problem in terms of classical linear mixed models. The transformed likelihood approach is based on the first differences data transformation, the following results derive from a convenient reformulation in terms of deviations from the first observations. Given the invariance to data transformation, the likelihood functions defined in the two cases coincide. Resulting in a classical random effect linear model form, the proposed approach significantly improves the number of available estimation procedures and provides a straightforward interpretation for the parameters. Moreover, the proposed model specification allows to consider all the estimation improvements typical of the random effects model literature. Simulation studies are conducted in order to study the robustness of the estimation method to mean stationarity violation.
机译：用于估计固定效果动态面板数据模型的变换似然方法显示出了很好的推论属性，但是并没有在最分散的统计软件中直接实现。本文旨在展示如何采用简单的模型重构来描述经典线性混合模型方面的问题。变换似然法基于第一差异数据变换，以下结果是从与第一观测值的偏差方面方便的重新表述得出的。给定数据转换的不变性，在两种情况下定义的似然函数一致。产生经典的随机效应线性模型形式，所提出的方法显着提高了可用估计程序的数量，并为参数提供了直接的解释。此外，提出的模型规范允许考虑随机效应模型文献中所有典型的估计改进。为了研究估计方法对平稳性违例的鲁棒性，进行了仿真研究。

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