ENHANCING INFLATION FORECASTING THROUGH HYBRID ECONOMETRIC AND MACHINE LEARNING APPROACHES IN EMERGING MARKETS
DOI:
https://doi.org/10.47390/ydif-y2025v1i9/n06Ключевые слова:
Inflation Forecasting, Econometrics, Machine Learning, Hybrid Models, Emerging Markets, Monetary Policy, Predictive Accuracy.Аннотация
This study explores the enhancement of inflation forecasting accuracy in emerging markets through a hybrid framework combining econometric and machine learning approaches. Traditional models such as ARIMA and VAR provide theoretical interpretability but struggle to capture nonlinear dynamics and structural shifts typical of volatile economies. To address this, the research integrates econometric techniques with machine learning algorithms including Random Forest, Gradient Boosting, and LSTM networks. Using monthly macroeconomic data from 2000 to 2024, the hybrid ARIMA–LSTM and VAR–Random Forest models achieved 15–25% lower forecasting errors than traditional methods. The results indicate that hybrid models effectively balance interpretability and predictive accuracy, offering practical tools for policymakers to design timely and data-driven monetary policies. The study emphasizes the importance of data quality, institutional capacity, and interdisciplinary skills to implement such models effectively in emerging economies.
Библиографические ссылки
1. Athey S. The Impact of Machine Learning on Economics // In: Agrawal A., Gans J., and Goldfarb A. (eds.) The Economics of Artificial Intelligence. – Chicago: University of Chicago Press, 2018. – P. 507–547.
2. Box G.E.P., Jenkins G.M. Time Series Analysis: Forecasting and Control. – San Francisco: Holden-Day, 1976. – P. 35–36.
3. Breiman L. Random Forests // Machine Learning. – 2001. – Vol. 45(1). – P. 5–32.
4. Cerra V., Fatás A., Saxena S.C. Hysteresis and Business Cycles // Journal of Economic Literature. – 2022. – Vol. 60(1). – P. 1–45.
5. Clements M.P., Hendry D.F. Forecasting Economic Time Series. – Cambridge: Cambridge University Press, 1998. – P. 25–26.
6. Diebold F.X., Mariano R.S. Comparing Predictive Accuracy // Journal of Business and Economic Statistics. – 1995. – Vol. 13(3). – P. 253–263.
7. Engle R.F., Granger C.W.J. Co-integration and Error Correction: Representation, Estimation, and Testing // Econometrica. – 1987. – Vol. 55(2). – P. 251–276.
8. Friedman J.H. Greedy Function Approximation: A Gradient Boosting Machine // Annals of Statistics. – 2001. – Vol. 29(5). – P. 1189–1232.
9. Hamilton J.D. Time Series Analysis. – Princeton: Princeton University Press, 1994. – P. 57–58.
10. Hochreiter S., Schmidhuber J. Long Short-Term Memory // Neural Computation. – 1997. – Vol. 9(8). – P. 1735–1780.
11. Hyndman R.J., Athanasopoulos G. Forecasting: Principles and Practice (2nd ed.). – Melbourne: OTexts, 2018. – P. 22–23.
12. International Monetary Fund (IMF). World Economic Outlook: Navigating Global Divergences. – Washington, D.C.: IMF Publications, 2024. – P. 64–65.
13. Kuzin V., Marcellino M., Schumacher C. Pooling versus Model Selection for Nowcasting GDP with Many Predictors: Empirical Evidence for Six Industrialized Countries // Journal of Applied Econometrics. – 2013. – Vol. 28(3). – P. 392–411.
14. Louzis D.P., Vouldis A.T., Metaxas V.L. Hybrid Machine Learning–Econometric Models for Forecasting Macroeconomic Variables // Economic Modelling. – 2021. – Vol. 98. – P. 105–124.
15. McNelis P.D. Neural Networks in Finance: Gaining Predictive Edge in the Market. – Amsterdam: Elsevier Academic Press, 2005. – P. 33–34.
16. Melecky M., Raddatz C. Fiscal Policy in Emerging Markets and Developing Economies: Growth, Volatility, and Institutions. – World Bank Policy Research Working Paper No. 7377. – Washington, D.C.: The World Bank, 2015. – P. 30–31.
17. Sims C.A. Macroeconomics and Reality // Econometrica. – 1980. – Vol. 48(1). – P. 1–48.
18. Stock J.H., Watson M.W. Forecasting Inflation // Journal of Monetary Economics. – 1999. – Vol. 44(2). – P. 293–335.
19. World Bank. Global Economic Prospects: Inflation Dynamics and Policy Challenges. – Washington, D.C.: The World Bank, 2023. – P. 45–47.
20. Zhang G.P. Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model // Neurocomputing. – 2003. – Vol. 50. – P. 159–175.
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