Forecasting with Bayesian Grouped Random Effects in Panel Data

Abstract

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 facilitates forecasts relative to standard panel data estimators.

Identifying latent groups in spatial panel data using a Markov random field constrained product partition model

“…Several more complicated models have also been proposed with different features, e.g., group-specific time patterns (Bonhomme and Manresa, 2015), time-varying grouped coefficients (Su et al, 2019) and group-varying threshold variables (Miao et al, 2020). Despite the success of those frequentist approaches, limited effort has been made under the Bayesian framework until very recently (Zhang, 2020). Conceptually, an ideal Bayesian approach would naturally be able to incorporate the spatial dependence and latent group structure information in the prior distribution.…”