Forecasting Banking System Liquidity Using Payment System Data in Uzbekistan

Shakhzod Abdullaevich Makhmudov
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Shakhzod Abdullaevich Makhmudov: The Central Bank of Uzbekistan

No 05-2025, IHEID Working Papers from Economics Section, The Graduate Institute of International Studies

Abstract: Forecasting banking system liquidity is crucial for the e ective monetary policy implementation. This study investigates the e ectiveness of various econometric and machine learning models in predicting the autonomous factors of banking system liquidity. The research compares widely used econometric models such as SARIMA, Exponential Smoothing, and Prophet alongside ma- chine learning models like Random Forest, applying various preprocessing techniques, including power transformations, scaling, and trend-cycle decomposition. Moreover, ensemble methods, like weighted blending and stacking, were used to improve accuracy. Experimental results in- dicate that SARIMA was the best individual model, but ensemble with Prophet and Random Forest further improved forecast performance. Neural network models underperformed poten- tially due to challenges in optimizing their architectures. Future research intends to explore multivariate and structural models, as well as advanced neural architectures, to enhance pre- dictive accuracy.

Keywords: Monetary Policy; Time-Series Models; Model Evaluation and Selection; Forecasting and Other Model Applications; Payment Systems (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 E42 E52 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2025-02-11, Revised 2025-02-17
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