Affiliations:
1Department of Economics, Mandakh University, Ulan Bator, Mongolia
2Department of Information Technology, Institute of Mathematics and Digital Technology, Ulan Bator, Mongolia
3Interdisciplinary Studies Department, University of Finance and Economics, Ulan Bator, Mongolia
4Department of Management, Management School, Mongolian University of Science and Technology, Ulan Bator, Mongolia
Accurate forecasting of central bank policy rates is essential for effective monetary policy, stable market expectations, and overall macroeconomic stability. In emerging economies such as Mongolia, traditional econometric models, including the Taylor Rule, ARIMA, and SVAR, often fail to adequately capture nonlinear relationships, time dependencies, and structural changes in the economy. To address these limitations, this study develops and evaluates advanced forecasting approaches based on hybrid combinations of machine learning and deep learning models. The analysis uses a monthly dataset consisting of 26 macroeconomic variables from January 2008 to December 2024. Seven forecasting models are constructed and evaluated using RMSE, MAE, and R² performance measures. The results indicate that hybrid models, particularly XGBoost combined with Gradient Boosting and LSTM integrated with XGBoost, achieve the highest forecasting accuracy, with the best model attaining an R² value of 0.9355. Overall, the hybrid approaches outperform both conventional econometric models and individual machine learning or deep learning models in capturing complex macroeconomic dynamics and structural shifts. These findings offer a reliable data-driven framework to support monetary policy decisions in Mongolia and provide a methodology that can be applied to other emerging economies with similar economic conditions.
Policy rate forecasting, Hybrid machine learning, Deep learning models, Monetary policy analysis, Emerging economies
https://doi.org/10.21833/ijaas.2026.01.025
Sodnomdavaa, T., Badrakh, O., Altangerel, D., & Sodnomdavaa, T. (2026). Forecasting central bank policy rates using machine learning and deep learning approaches. International Journal of Advanced and Applied Sciences, 13(1), 239–246. https://doi.org/10.21833/ijaas.2026.01.025