Dynamic Bayesian Networks for Modelling Liquidity Preference-Money Supply

Emre Yilmaz, Selin Demir, Aylin Karaca () and Lila Moore
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Lila Moore: Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA

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Abstract: This paper proposes the utilization of Dynamic Bayesian Networks for modeling Liquidity Preference-Money Supply, aiming to address the pressing need for advanced tools to analyze economic dynamics. The current research landscape lacks efficient methods to account for the intricate relationships and uncertainties inherent in monetary systems, posing significant challenges for accurate modeling and forecasting. In response, this study introduces a novel approach that leverages Dynamic Bayesian Networks to capture the complex interactions between liquidity preferences and money supply, offering a more comprehensive and adaptable framework for economic analysis. By integrating this innovative methodology, the paper advances the understanding of monetary dynamics and provides valuable insights for policymakers and researchers in the field.

Keywords: Dynamic Bayesian Networks Liquidity Preference Money Supply Economic Analysis Monetary Dynamics; Dynamic Bayesian Networks; Liquidity Preference; Money Supply; Economic Analysis; Monetary Dynamics (search for similar items in EconPapers)
Date: 2025-02-23
Note: View the original document on HAL open archive server: https://hal.science/hal-05086735v1
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Published in ECONOMIC AND FINANCIAL RESEARCH LETTERS, 2025

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