EVALUATING THE EFFICIENCY OF HIERARCHICAL BAYESIAN ESTIMATORS IN DYNAMIC PANEL DATA MODEL

Abstract

Using simulated datasets under various panel structures (NT), this work assesses the effectiveness of hierarchical Bayesian estimators in dynamic panel data models. The parameters of models with lagged response variables and stochastic errors were estimated under informative priors using Monte Carlo techniques and Markov Chain Monte Carlo (MCMC) simulations.

Across all panel designs, the results show that model precision increases with sample size; the most consistent estimator gains occur when the number of cross-sectional units surpasses time periods (N>T). The computational expense of high-dimensional Bayesian estimation is highlighted by the fact that this gain is accompanied by an increase in numerical standard error (NSE).

This work confirms the robustness of hierarchical Bayesian techniques for dynamic panel analysis, especially in large and imbalanced datasets. The results offer practical insights for researchers and policymakers modeling economic and social processes. Future research can extend this framework to non-linear models, alternative prior structures, and real-world applications.