Bayesian Hierarchical Modeling for Inflation Forecasting in Emerging Economies
DOI:
https://doi.org/10.14419/gsws6e80Keywords:
Bayesian Hierarchical Model; Inflation Forecasting; Emerging Economies; Hamiltonian Monte Carlo; Macroeconomic UncertaintyAbstract
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.
References
- T. E. Clark, "Forecasting with Bayesian vector autoregressions," Federal Reserve Bank of Kansas City Working Paper, no. 11-01, 2011.
- M. Del Negro and F. Schorfheide, "Bayesian macroeconometrics," in The Oxford Handbook of Bayesian Econometrics, J. Geweke, G. Koop, and H. van Dijk, Eds. Oxford, U.K.: Oxford Univ. Press, 2011, pp. 293–389. https://doi.org/10.1093/oxfordhb/9780199559084.013.0008
- A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin, Bayesian Data Analysis, 3rd ed. Boca Raton, FL, USA: Chapman & Hall/CRC, 2013. https://doi.org/10.1201/b16018.
- D. Giannone, M. Lenza, and G. E. Primiceri, "Prior selection for vector autoregressions," Rev. Econ. Stat., vol. 97, no. 2, pp. 436–451, 2015. https://doi.org/10.1162/REST_a_00483.
- M. Kalli and J. E. Griffin, "Bayesian hierarchical modeling for macroeconomic time series," J. Appl. Econometrics, vol. 35, no. 1, pp. 123–147, 2020.
- S. Kozicki and P. A. Tinsley, "Permanent and transitory policy shocks in an empirical macro model with asymmetric information," J. Econ. Dyn. Control, vol. 29, no. 11, pp. 1985–2015, 2005. https://doi.org/10.1016/j.jedc.2005.06.003.
- A. S. H. Raheem, "Selecting the most important variables affecting iron deficiency in the blood using the Bayesian elastic network method," Warith Sci. J., vol. 6, pp. 1064–1072, 2024.
- S. H. Raheem, "Use Box–Jenkins strategy for making a time sample to forecasting to oil prices," Psychol. Educ., vol. 58, no. 2, pp. 9943–9951, 2021.
- S. H. Raheem and R. H. Mahdi, "Employing Bayesian Lasso with Sliced Inverse Regression for high-dimensional data analysis," Al-Qadisiyah J. Admin. Econ. Sci., vol. 26, no. 4, pp. 97–106, 2025.
- J. C. Robertson and E. W. Tallman, "Vector autoregressions: Forecasting and structural analysis," Federal Reserve Bank of Atlanta Working Paper Series, no. 2018-12, 2018.
- C. A. Sims and T. Zha, "Bayesian methods for dynamic multivariate models," Int. Econ. Rev., vol. 39, no. 4, pp. 949–968, 1998. https://doi.org/10.2307/2527347.
- J. H. Stock and M. W. Watson, "Why has U.S. inflation become harder to forecast?," J. Money Credit Bank., vol. 39, no. s1, pp. 3–33, 2007. https://doi.org/10.1111/j.1538-4616.2007.00014.x.
- D. F. Waggoner and T. Zha, "Gibbs sampler for structural vector autoregressions," J. Econ. Dyn. Control, vol. 31, no. 2, pp. 507–530, 2007.