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Nonlinear micro income processes with macro shocks

Author

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  • Martín Almuzara
  • Manuel Arellano
  • Richard Blundell
  • Stéphane Bonhomme

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," CeMMAP working papers 17/25, Institute for Fiscal Studies.
  • Handle: RePEc:azt:cemmap:17/25
    DOI: 10.47004/wp.cem.2025.1725
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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. 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.
    3. Manuel Arellano & Stéphane Bonhomme, 2016. "Nonlinear panel data estimation via quantile regressions," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 61-94, October.
    4. Thomas Winberry, 2018. "A method for solving and estimating heterogeneous agent macro models," Quantitative Economics, Econometric Society, vol. 9(3), pages 1123-1151, November.
    5. 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.
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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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