Modelling Dynamic Panel Data using Hierarchical Bayesian Approach

Abstract

This study developed a hierarchical Bayesian framework for dynamic panel data models to address the challenges of unequal variances regularly encountered in empirical research. Dynamic panel data models with temporal dynamics were estimated using OLS and GMM. It was discovered that estimated parameters were biased, inconsistent and unreliable. The developed estimator proved to be robust in addressing the shortfall and outperformed other estimators under various panel data configurations. Estimating a robust hierarchical Bayesian model for dynamic panel and assessing its performance under various panel structure (N<T, N=T, N>T) of cross-sectional units (N) and time dimensions (T). The framework provides great flexibility in handling heteroscedasticity and diverse panel systems by using several Markov Chain Monte Carlo (MCMC) experiments were carried out to asses the performances of the estimators are gauge the suitability of posterior estimators. It was discovered that the developed estimator exhibited numerical standard errors and posterior values which closely match fixed parameter values. Sensitivity analyses also demonstrate the significance of prior specification, showing that notably informative priors improve estimation accuracy. This work addresses key limitations of classical and Bayesian methods, providing practical framework for modelling dynamic panel data. Its application amplifies to various empirical contexts, making it a treasured tool for researchers managing datasets with unequal error variances.