Forecasting with Bayesian Grouped Random Effects in Panel Data

TL;DR

This paper introduces a Bayesian grouped random effects model for panel data that identifies latent group structures to improve forecasting accuracy, demonstrating superior performance over standard methods in simulations and empirical investment rate forecasts.

Contribution

It develops a nonparametric Bayesian approach to simultaneously estimate coefficients and latent group membership, enhancing forecast accuracy by incorporating subjective prior knowledge.

Findings

Bayesian grouped random effects estimators outperform standard panel data methods in simulations.

The method accurately identifies latent group structures comparable to Kmeans clustering.

Empirical application shows improved investment rate forecasts using the estimated group structure.

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 improves forecasts relative to standard panel data estimators.