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<p>An important macroeconomics task is identifying variable change patterns through mathematical modeling. To delve into the interconnections among macroeconomic variables, this study employs two data science methods: Transfer Entropy (TE) and Sparse Identification Nonlinear Dynamics Control (SINDyC). TE is utilized to select variables and explore their relationships, while SINDyC extracts a multi-dimensional nonlinear dynamics model and analyzes the stability of the model using phase plane analysis. This paper offers a perspective on the relationship between macroeconomic variables by identifying stable areas within the system. The results could help policymakers gain valuable insights and a deeper understanding of the interactions among nonlinear dynamics in system variables. The results emphasize various interest rate selection scenarios to demonstrate stability regions of the inflation and unemployment rate, as influenced by different entries of GDP per capita. Phase plane to begin with result recognizes stable areas within the unemployment-inflation relationship, unveiling nuanced dynamics within distinctive GDP per capita and interest rate scenarios. In contrast, a steady GDP per capita zone is built up over shifted scenarios of the inflation rate and interest rate in the phase plane second simulation results. The third result in the phase plane claimed a stable pattern of the inflation rate versus GDP per capita, considering various constants of the interest rate. Furthermore, this new perspective offers a valuable approach to quantitative macroeconomic analysis, providing policymakers with comprehensive data that can enhance their understanding and decision-making processes.</p>

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Introducing a Nonlinear Macroeconomic Model Based on TE, SINDYC, and Phase Plane Analysis

  • 期刊论文

  • 发表日期：

    2025年7月3日

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  • Milad Yazdanpanah,
  • Bijan Moaveni,
  • Mehdi Siahi,
  • Sasan Barak,
  • Soodabe Soleimani,
  • Hirsa Kia

摘要

An important macroeconomics task is identifying variable change patterns through mathematical modeling. To delve into the interconnections among macroeconomic variables, this study employs two data science methods: Transfer Entropy (TE) and Sparse Identification Nonlinear Dynamics Control (SINDyC). TE is utilized to select variables and explore their relationships, while SINDyC extracts a multi-dimensional nonlinear dynamics model and analyzes the stability of the model using phase plane analysis. This paper offers a perspective on the relationship between macroeconomic variables by identifying stable areas within the system. The results could help policymakers gain valuable insights and a deeper understanding of the interactions among nonlinear dynamics in system variables. The results emphasize various interest rate selection scenarios to demonstrate stability regions of the inflation and unemployment rate, as influenced by different entries of GDP per capita. Phase plane to begin with result recognizes stable areas within the unemployment-inflation relationship, unveiling nuanced dynamics within distinctive GDP per capita and interest rate scenarios. In contrast, a steady GDP per capita zone is built up over shifted scenarios of the inflation rate and interest rate in the phase plane second simulation results. The third result in the phase plane claimed a stable pattern of the inflation rate versus GDP per capita, considering various constants of the interest rate. Furthermore, this new perspective offers a valuable approach to quantitative macroeconomic analysis, providing policymakers with comprehensive data that can enhance their understanding and decision-making processes.