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Inflation cycles: evidence from international data

Author

Listed:
  • Alberto Americo
  • Douglas Kiarelly Godoy de Araujo
  • Johannes Damp
  • Sjur Nilsen
  • Daniel Rees
  • Rafael Schmidt
  • Christian Schmieder

Abstract

We identify and document key stylised facts of inflation cycles for a large panel of advanced and emerging market economies. To this end, we propose three complementary inflation cycle concepts: (1) cycles in inflation levels, reflecting mostly the low- and medium-frequency components of inflation; (2) cycles in higher-frequency deviation of inflation from its trend; and (3) a categorisation of inflation into high and low inflation regimes. For each concept, we document key stylised facts within and across countries and examine how these have evolved over time. We also show that the relationship between inflation and business cycles matters: entry in a high-inflation regime is associated with a significantly higher chance of a recession in the following quarters. A cross-country dataset with the inflation cycles is made publicly available.

Suggested Citation

  • Alberto Americo & Douglas Kiarelly Godoy de Araujo & Johannes Damp & Sjur Nilsen & Daniel Rees & Rafael Schmidt & Christian Schmieder, 2025. "Inflation cycles: evidence from international data," BIS Working Papers 1264, Bank for International Settlements.
    • Handle: RePEc:bis:biswps:1264
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    References listed on IDEAS

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    More about this item

    Keywords

    inflation cycles; business cycle; monetary policy;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • F44 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - International Business Cycles
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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