IDEAS home Printed from https://ideas.repec.org/p/cmf/wpaper/wp2025_2515.html
   My bibliography  Save this paper

Nonlinear Micro Income Processes with Macro Shocks

Author

Listed:

Abstract

We propose a nonlinear framework to study the dynamic transmission of aggregate and idiosyncratic shocks to household income that exploits both macro and micro data. Our approach allows us to examine empirically the following questions: (a) How do business-cycle fluctuations modulate the persistence of heterogeneous individual histories and the risk faced by households? (b) How do aggregate and idiosyncratic shocks propagate over time for households in different macro and micro states? (c) How do these shocks shape the cost of business-cycle risk? We develop new identification and estimation techniques, and provide a detailed empirical analysis combining macro time series for the U.S. and a time series of household panels from the PSID.

Suggested Citation

  • Martín Almuzara & Manuel Arellano & Richard Blundell & Stéphane Bonhomme, 2025. "Nonlinear Micro Income Processes with Macro Shocks," Working Papers wp2025_2515, CEMFI.
  • Handle: RePEc:cmf:wpaper:wp2025_2515
    as

    Download full text from publisher

    File URL: https://www.cemfi.es/ftp/wp/2515.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Manuel Arellano & Stéphane Bonhomme, 2016. "Nonlinear panel data estimation via quantile regressions," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 61-94, October.
    2. Arellano, Manuel & Blundell, Richard & Bonhomme, Stéphane & Light, Jack, 2024. "Heterogeneity of consumption responses to income shocks in the presence of nonlinear persistence," Journal of Econometrics, Elsevier, vol. 240(2).
    3. Thomas Winberry, 2018. "A method for solving and estimating heterogeneous agent macro models," Quantitative Economics, Econometric Society, vol. 9(3), pages 1123-1151, November.
    4. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
    5. Christian Bayer & Ralph Luetticke, 2020. "Solving discrete time heterogeneous agent models with aggregate risk and many idiosyncratic states by perturbation," Quantitative Economics, Econometric Society, vol. 11(4), pages 1253-1288, November.
    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. Yujie Yang & Chenxing Zhang & Wenwen Hou, 2023. "Two-Country HANK and trade friction," PLOS ONE, Public Library of Science, vol. 18(7), pages 1-37, July.
    2. Sagiri KITAO & Michio SUZUKI & Tomoaki YAMADA, 2025. "Nonlinear Earnings Dynamics and Inequality over the Life Cycle: Evidence from Japanese Municipal Tax Records," Discussion papers 25081, Research Institute of Economy, Trade and Industry (RIETI).
    3. Laura Liu & Mikkel Plagborg‐Møller, 2023. "Full‐information estimation of heterogeneous agent models using macro and micro data," Quantitative Economics, Econometric Society, vol. 14(1), pages 1-35, January.
    4. Langot, François & Malmberg, Selma & Tripier, Fabien & Hairault, Jean-Olivier, 2023. "The Macroeconomic and Redistributive Effects of Shielding Consumers from Rising Energy Prices: the French Experiment," CEPREMAP Working Papers (Docweb) 2305, CEPREMAP.
    5. Juan Carlos Parra‐Alvarez & Olaf Posch & Mu‐Chun Wang, 2023. "Estimation of Heterogeneous Agent Models: A Likelihood Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 304-330, April.
    6. Laura Liu & Mikkel Plagborg-M{o}ller, 2021. "Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data," Papers 2101.04771, arXiv.org, revised Jun 2022.
    7. Donggyu Lee, 2024. "Unconventional Monetary Policies and Inequality," Staff Reports 1108, Federal Reserve Bank of New York.
    8. Drew Creal & Siem Jan Koopman & Eric Zivot, 2008. "The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model," Tinbergen Institute Discussion Papers 08-069/4, Tinbergen Institute.
    9. Davide Pettenuzzo & Francesco Ravazzolo, 2016. "Optimal Portfolio Choice Under Decision‐Based Model Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
    10. John Carter Braxton & Kyle F. Herkenhoff & Jonathan Rothbaum & Lawrence Schmidt, 2021. "Changing Income Risk across the US Skill Distribution: Evidence from a Generalized Kalman Filter," Opportunity and Inclusive Growth Institute Working Papers 55, Federal Reserve Bank of Minneapolis.
    11. Takahashi, Makoto & Watanabe, Toshiaki & Omori, Yasuhiro, 2016. "Volatility and quantile forecasts by realized stochastic volatility models with generalized hyperbolic distribution," International Journal of Forecasting, Elsevier, vol. 32(2), pages 437-457.
    12. Jorge E. Galán & María Rodríguez Moreno, 2020. "At-risk measures and financial stability," Financial Stability Review, Banco de España, issue Autumn.
    13. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    14. Martín Almuzara & Richard Audoly & Davide Melcangi, 2025. "A Measure of Trend Wage Inflation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(5), pages 508-520, August.
    15. Richard K. Crump & Stefano Eusepi & Domenico Giannone & Eric Qian & Argia M. Sbordone, 2021. "A Large Bayesian VAR of the United States Economy," Staff Reports 976, Federal Reserve Bank of New York.
    16. Arellano, Manuel & Blundell, Richard & Bonhomme, Stéphane & Light, Jack, 2024. "Heterogeneity of consumption responses to income shocks in the presence of nonlinear persistence," Journal of Econometrics, Elsevier, vol. 240(2).
    17. Xianguo HUANG & Roberto LEON-GONZALEZ & Somrasri YUPHO, 2013. "Financial Integration from a Time-Varying Cointegration Perspective," Asian Journal of Empirical Research, Asian Economic and Social Society, vol. 3(12), pages 1473-1487.
    18. Koopman, Siem Jan & Lucas, André, 2008. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 510-525.
    19. McCausland, William J. & Miller, Shirley & Pelletier, Denis, 2011. "Simulation smoothing for state-space models: A computational efficiency analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 199-212, January.
    20. Zhouzhou Gu & Mathieu Lauri`ere & Sebastian Merkel & Jonathan Payne, 2024. "Global Solutions to Master Equations for Continuous Time Heterogeneous Agent Macroeconomic Models," Papers 2406.13726, arXiv.org.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    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:cmf:wpaper:wp2025_2515. 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: Araceli Requerey (email available below). General contact details of provider: https://edirc.repec.org/data/cemfies.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.