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Nonlinear micro income processes with macro shocks

Author

Listed:
  • Martín Almuzara

    (Federal Reserve Bank of New York)

  • Manuel Arellano

    (Institute for Fiscal Studies)

  • Richard Blundell

    (Institute for Fiscal Studies)

  • Stéphane Bonhomme

    (Institute for Fiscal Studies)

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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Martín Almuzara & Manuel Arellano & Richard Blundell & Stéphane Bonhomme, 2025. "Nonlinear micro income processes with macro shocks," IFS Working Papers WCWP17/25, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:ifsewp:cwp17/25
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    File URL: https://ifs.org.uk/sites/default/files/2025-08/CWP1725-Nonliear-micro-income-processes-with-macro-shocks.pdf
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    References listed on IDEAS

    as
    1. 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).
    2. Thomas Winberry, 2018. "A method for solving and estimating heterogeneous agent macro models," Quantitative Economics, Econometric Society, vol. 9(3), pages 1123-1151, November.
    3. 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.
    4. Manuel Arellano & Stéphane Bonhomme, 2016. "Nonlinear panel data estimation via quantile regressions," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 61-94, October.
    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)

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    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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