Bayesian Hierarchical Modeling for Inflation Forecasting in Emerging Economies

This paper develops a Bayesian hierarchical model to forecast inflation rates in emerging economies, incorporating structural ‎heterogeneity across countries. Unlike traditional models, the proposed approach allows for both country-specific dynamics and ‎global information sharing. The model is implemented using Hamiltonian Monte Carlo methods and evaluated through a simulation ‎study and real data analysis involving Iraq, Egypt, Turkey, and India. Key macroeconomic predictors include exchange rates, interest ‎rates, and oil prices. The results demonstrate that the hierarchical model outperforms conventional approaches such as ARIMA and ‎VAR in terms of forecast accuracy, parameter stability, and uncertainty quantification. This highlights the model’s potential for more ‎informed macroeconomic planning in volatile and data-constrained environments‎.