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Nonlinearities and heterogeneity in firms response to aggregate fluctuations: what can we learn from machine learning?

Author

Listed:
  • Pesce, Simone
  • Errico, Marco
  • Pollio, Luigi

Abstract

Firms respond heterogeneously to aggregate fluctuations, yet standard linear models impose restrictive assumptions on firm sensitivities. Applying the Generalized Random Forest to U.S. firm-level data, we document strong nonlinearities in how firm characteristics shape responses to macroeconomic shocks. We show that nonlinearities significantly lower aggregate esponses, leading linear models to overestimate the economy’s sensitivity to shocks by up to 1.7 percentage points. We also find that larger firms, which carry disproportionate economic weight, exhibit lower sensitivities, leading to a median reduction in aggregate economic sensitivity of 52%. Our results highlight the importance of accounting for nonlinearities and firm heterogeneity when analyzing macroeconomic fluctuations and the transmission of aggregate shocks. JEL Classification: D22, E32, C14, E5

Suggested Citation

  • Pesce, Simone & Errico, Marco & Pollio, Luigi, 2025. "Nonlinearities and heterogeneity in firms response to aggregate fluctuations: what can we learn from machine learning?," Working Paper Series 3107, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20253107
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    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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