Forecasting with Bayesian Grouped Random Effects in Panel Data
88 Pages Posted: 15 Oct 2020 Last revised: 19 Oct 2020
Date Written: August 26, 2020
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.
Keywords: Panel Data; Group Heterogeneity, Random Effects, Dirichlet Process, Set Forecast, Density forecast, Investment
JEL Classification: C11, C14, C23, C53, G31
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