Panel data are collected over several time periods for the same units and hence allow for modelling both latent heterogeneity and dynamics. Since in a dynamic setup, the dependent variable also appears as an explanatory variable in later periods, missing values lead to substantial loss of information and the possibility of inefficient estimation. For linear dynamic panel models with fixed or random effects, we suggest a Bayesian approach to deal with missing values. The Gibbs sampling scheme providing a sample from the posterior distribution is thereby augmented by draws from the full conditional distribution of the missing values. While the full conditional distribution for missing values in the dependent variable is implied by the model setup, we incorporate a flexible non‐parametric approximation to the full conditional posterior distribution of missing values in the explaining variables. Also, we provide accurate non‐nested model comparison in terms of the marginal likelihood from the resulting hybrid Gibbs sampling output. The properties and possible efficiency gains of the suggested approach are illustrated by means of a simulation study and an empirical application using a macroeconomic panel data set.

中文翻译：

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缺失值的线性动态面板模型的贝叶斯估计和模型比较

在相同单元的多个时间段内收集面板数据，因此可以对潜在的异质性和动力学进行建模。由于在动态设置中，因变量在以后的周期中也将作为解释变量出现，因此缺少值会导致信息的大量丢失和估计效率低下的可能性。对于具有固定或随机效应的线性动态面板模型，我们建议使用贝叶斯方法来处理缺失值。从后验分布中提供样本的吉布斯采样方案因此通过从缺失值的全部条件分布中抽取来增加。虽然模型设置隐含了因变量中缺失值的完整条件分布，我们在解释变量中对缺失值的全部条件后验分布采用了一种灵活的非参数近似法。此外，就混合吉布斯采样输出的边际可能性而言，我们提供了准确的非嵌套模型比较。通过模拟研究和使用宏观经济面板数据集的经验应用，说明了所建议方法的性质和可能的效率提高。