IDEAS home Printed from https://ideas.repec.org/p/ecb/ecbwps/20253107.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp3107~5dac630327.en.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Refet Gürkaynak & Hati̇ce Gökçe Karasoy‐Can & Sang Seok Lee, 2022. "Stock Market's Assessment of Monetary Policy Transmission: The Cash Flow Effect," Journal of Finance, American Finance Association, vol. 77(4), pages 2375-2421, August.
    2. Michael D. Bauer & Eric T. Swanson, 2023. "A Reassessment of Monetary Policy Surprises and High-Frequency Identification," NBER Macroeconomics Annual, University of Chicago Press, vol. 37(1), pages 87-155.
    3. Pasten, Ernesto & Schoenle, Raphael & Weber, Michael, 2020. "The propagation of monetary policy shocks in a heterogeneous production economy," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 1-22.
    4. Joachim Freyberger & Andreas Neuhierl & Michael Weber & Andrew KarolyiEditor, 2020. "Dissecting Characteristics Nonparametrically," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
    5. James H. Stock & Mark W. Watson, 2018. "Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments," Economic Journal, Royal Economic Society, vol. 128(610), pages 917-948, May.
    6. Michael Weber & Ali Ozdagli, 2016. "Monetary Policy Through Production Networks: Evidence from the Stock Market," 2016 Meeting Papers 148, Society for Economic Dynamics.
    7. Minsu Chang & Xiaohong Chen & Frank Schorfheide, 2024. "Heterogeneity and Aggregate Fluctuations," Journal of Political Economy, University of Chicago Press, vol. 132(12), pages 4021-4067.
    8. Joachim Jungherr & Matthias Meier & Timo Reinelt & Immo Schott, 2022. "Corporate Debt Maturity Matters For Monetary Policy," CRC TR 224 Discussion Paper Series crctr224_2022_360, University of Bonn and University of Mannheim, Germany.
    9. Michael Weber & Ali Ozdagli, 2016. "Monetary Policy Through Production Networks: Evidence from the Stock Market," 2016 Meeting Papers 148, Society for Economic Dynamics.
    10. Iván Alfaro & Nicholas Bloom & Xiaoji Lin, 2024. "The Finance Uncertainty Multiplier," Journal of Political Economy, University of Chicago Press, vol. 132(2), pages 577-615.
    11. Narayan, Paresh Kumar & Sharma, Susan Sunila, 2011. "New evidence on oil price and firm returns," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3253-3262.
    12. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    13. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    14. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    15. Joan Farre-Mensa & Alexander Ljungqvist, 2016. "Do Measures of Financial Constraints Measure Financial Constraints?," The Review of Financial Studies, Society for Financial Studies, vol. 29(2), pages 271-308.
    16. A. Belloni & V. Chernozhukov & I. Fernández‐Val & C. Hansen, 2017. "Program Evaluation and Causal Inference With High‐Dimensional Data," Econometrica, Econometric Society, vol. 85, pages 233-298, January.
    17. Pablo Ottonello & Thomas Winberry, 2020. "Financial Heterogeneity and the Investment Channel of Monetary Policy," Econometrica, Econometric Society, vol. 88(6), pages 2473-2502, November.
    18. Francisco Covas & Wouter J. Den Haan, 2011. "The Cyclical Behavior of Debt and Equity Finance," American Economic Review, American Economic Association, vol. 101(2), pages 877-899, April.
    19. Nicolas Crouzet, 2018. "Aggregate Implications of Corporate Debt Choices," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(3), pages 1635-1682.
    20. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    21. Deng, Minjie & Fang, Min, 2022. "Debt maturity heterogeneity and investment responses to monetary policy," European Economic Review, Elsevier, vol. 144(C).
    22. Lenza, Michele & Savoia, Ettore, 2024. "Do we need firm data to understand macroeconomic dynamics?," Working Paper Series 438, Sveriges Riksbank (Central Bank of Sweden).
    23. Saten Kumar & Yuriy Gorodnichenko & Olivier Coibion, 2023. "The Effect of Macroeconomic Uncertainty on Firm Decisions," Econometrica, Econometric Society, vol. 91(4), pages 1297-1332, July.
    24. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    25. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
    26. Francisco J. Buera & Benjamin Moll, 2015. "Aggregate Implications of a Credit Crunch: The Importance of Heterogeneity," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(3), pages 1-42, July.
    27. Nicolas Crouzet & Neil R. Mehrotra, 2020. "Small and Large Firms over the Business Cycle," American Economic Review, American Economic Association, vol. 110(11), pages 3549-3601, November.
    28. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    29. Juliane Begenau & Juliana Salomao, 2019. "Firm Financing over the Business Cycle," The Review of Financial Studies, Society for Financial Studies, vol. 32(4), pages 1235-1274.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Priit Jeenas, 2023. "Firm Balance Sheet Liquidity, Monetary Policy Shocks, and Investment Dynamics," Working Papers 1409, Barcelona School of Economics.
    2. Poeschl, Johannes, 2023. "Corporate debt maturity and investment over the business cycle," European Economic Review, Elsevier, vol. 152(C).
    3. Bianco, Timothy & Herrera, Ana María, 2025. "Monetary policy and credit flows: A tale of two effective lower bounds," Journal of Economic Dynamics and Control, Elsevier, vol. 175(C).
    4. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    5. Afonso S. Moura & Gergely Buda & Vasco M. Carvalho & Giancarlo Corsetti & João B. Duarte & Stephen Hansen & Álvaro Ortiz & Tomasa Rodrigo & José V. Rodríguez Mora & Guilherme Alves da Silva, 2025. "The Short Lags of Monetary Policy," Working Papers w202501, Banco de Portugal, Economics and Research Department.
    6. Gergely Buda & Vasco M. Carvalho & Giancarlo Corsetti & João Duarte & Stephen Hansen & Afonso Pereira da Silva & Alvaro Ortiz & Tomasa Rodrigo & Guilherme Alves da Silva & José V. Rodríguez Mora, 2025. "España | Los cortos retardos de la política monetaria [Spain | The Short Lags of Monetary Policy]," Working Papers 25/02, BBVA Bank, Economic Research Department.
    7. Yulin Liu & Luyao Zhang, 2022. "Cryptocurrency Valuation: An Explainable AI Approach," Papers 2201.12893, arXiv.org, revised Jul 2023.
    8. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
    9. Branco, Rafael R. & Rubesam, Alexandre & Zevallos, Mauricio, 2024. "Forecasting realized volatility: Does anything beat linear models?," Journal of Empirical Finance, Elsevier, vol. 78(C).
    10. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    11. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    12. BBVA Research, 2025. "España | Los breves desfases de la Política Monetaria [Spain | The Short Lags of Monetary Policy]," Working Papers 25/08, BBVA Bank, Economic Research Department.
    13. Darmouni, Olivier & Geisecke, Oliver & Rodnyanky, Alexander, 2019. "The Bond Lending Channel of Monetary Policy," MPRA Paper 95141, University Library of Munich, Germany.
    14. Tom L. Dudda & Lars Hornuf, 2025. "The Perks and Perils of Machine Learning in Business and Economic Research," CESifo Working Paper Series 11721, CESifo.
    15. Gareth Anderson & Ambrogio Cesa-Bianchi, 2020. "Crossing the credit channel: credit spreads and firm heterogeneity," Bank of England working papers 854, Bank of England.
    16. Pan, Shuiyang & Long, Suwan(Cheng) & Wang, Yiming & Xie, Ying, 2023. "Nonlinear asset pricing in Chinese stock market: A deep learning approach," International Review of Financial Analysis, Elsevier, vol. 87(C).
    17. Emanuel Kohlscheen, 2022. "Quantifying the Role of Interest Rates, the Dollar and Covid in Oil Prices," Papers 2208.14254, arXiv.org, revised Oct 2022.
    18. Hanauer, Matthias X. & Kononova, Marina & Rapp, Marc Steffen, 2022. "Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets," Finance Research Letters, Elsevier, vol. 48(C).
    19. Huang, Yi & Lin, Chen & Liu, Sibo & Tang, Heiwai, 2023. "Trade networks and firm value: Evidence from the U.S.-China trade war," Journal of International Economics, Elsevier, vol. 145(C).
    20. Marie Alder & Nuno Coimbra & Urszula Szczerbowicz, 2025. "Corporate debt structure and heterogeneous monetary policy transmission," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Leveraging corporates' financial statements for policy insights, volume 65, Bank for International Settlements.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ecb:ecbwps:20253107. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Official Publications (email available below). General contact details of provider: https://edirc.repec.org/data/emieude.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.