论文标题
在面板数据中使用贝叶斯分组的随机效应进行预测
Forecasting with Bayesian Grouped Random Effects in Panel Data
论文作者
论文摘要
在本文中，我们估算并利用潜在的常数组结构来生成短动态面板数据的点，集合和密度预测。我们实施了一种非参数贝叶斯方法，以同时识别随机效应的系数和组成员身份，这些效应是各组之间异质性但固定在组中的异质性效应的。这种方法使我们能够灵活地纳入群体结构上的主观先验知识，从而有可能提高预测精度。在Monte Carlo实验中，我们证明了我们的贝叶斯分组随机效应（BGRE）估计器可产生准确的估计值，并比标准面板数据估计器得分预测性提高。凭借数据驱动的组结构，BGRE估计器与Kmeans算法的聚类表现出可比的精度，并且胜过两步的贝叶斯分组估计量，其组结构依赖于Kmeans。在经验分析中，我们应用我们的方法来预测广泛的公司的投资率，并说明估计的潜在群体结构改善了相对于标准面板数据估计器的预测。
In this paper, we estimate and leverage latent constant group structure to generate the point, set, and density forecasts for short dynamic panel data. We implement a nonparametric Bayesian approach to simultaneously identify coefficients and group membership in the random effects which are heterogeneous across groups but fixed within a group. This method allows us to flexibly incorporate subjective prior knowledge on the group structure that potentially improves the predictive accuracy. In Monte Carlo experiments, we demonstrate that our Bayesian grouped random effects (BGRE) estimators produce accurate estimates and score predictive gains over standard panel data estimators. With a data-driven group structure, the BGRE estimators exhibit comparable accuracy of clustering with the Kmeans algorithm and outperform a two-step Bayesian grouped estimator whose group structure relies on Kmeans. In the empirical analysis, we apply our method to forecast the investment rate across a broad range of firms and illustrate that the estimated latent group structure improves forecasts relative to standard panel data estimators.
