Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models

TL;DR

This paper introduces a Bayesian panel data model that incorporates prior knowledge of group structures through pairwise constraints, improving estimation accuracy and predictive performance.

Contribution

It develops a nonparametric Bayesian framework that explicitly models uncertainty in group partitions using prior pairwise constraints, enhancing traditional panel data models.

Findings

Prior knowledge improves coefficient estimation accuracy.

The method yields better density forecasts in CPI inflation prediction.

Identifies heterogeneous effects of income on democracy across countries.

Abstract

The assumption of group heterogeneity has become popular in panel data models. We develop a constrained Bayesian grouped estimator that exploits researchers' prior beliefs on groups in a form of pairwise constraints, indicating whether a pair of units is likely to belong to a same group or different groups. We propose a prior to incorporate the pairwise constraints with varying degrees of confidence. The whole framework is built on the nonparametric Bayesian method, which implicitly specifies a distribution over the group partitions, and so the posterior analysis takes the uncertainty of the latent group structure into account. Monte Carlo experiments reveal that adding prior knowledge yields more accurate estimates of coefficient and scores predictive gains over alternative estimators. We apply our method to two empirical applications. In a first application to forecasting U.S. CPI inflation, we illustrate that prior knowledge of groups improves density forecasts when the data is not entirely informative. A second application revisits the relationship between a country's income and its democratic transition; we identify heterogeneous income effects on democracy with five distinct groups over ninety countries.