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Mapping the space of central bankers' ideas

Author

Listed:
  • Taejin Park
  • Fernando Perez-Cruz
  • Hyun Song Shin

Abstract

This paper explores the landscape of economic ideas as revealed in the machine learning embedding of a comprehensive dataset of central bank speeches. This dataset, maintained by the BIS, encompasses 19,742 speeches delivered by almost 1,000 officials from over 100 central banks over a period spanning three decades, from 1997 to 2025. As well as topic analysis of speeches at any moment in time, the evolution of the topics over time provides insights into how the focus of central bank thinking has been shaped by shifting policy challenges since 1997. Parsing the embedding both through topics and through time provides rich insights into how economic ideas have taken shape through communication practices of central banks worldwide. To demonstrate its utility, we have conducted a series of analyses that map the global landscape of monetary policy discourse. Furthermore, we construct a quantitative framework-referred to as the "space of central bankers' ideas"-which uncovers institutional patterns and highlights shifts in policy approaches over time.

Suggested Citation

  • Taejin Park & Fernando Perez-Cruz & Hyun Song Shin, 2025. "Mapping the space of central bankers' ideas," BIS Working Papers 1299, Bank for International Settlements.
  • Handle: RePEc:bis:biswps:1299
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    More about this item

    Keywords

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    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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