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Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models

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  • Manuel Arellano

    (CEMFI, 28014 Madrid, Spain)

  • Stéphane Bonhomme

    (Department of Economics, University of Chicago, Chicago, Illinois 60637)

Abstract

Recent developments in nonlinear panel data analysis allow the identification and estimation of general dynamic systems. We review some results and techniques for nonparametric identification and flexible estimation in the presence of time-invariant and time-varying latent variables. This opens up the possibility of estimating nonlinear reduced forms in a large class of structural dynamic models with heterogeneous agents. We show how such reduced forms may be used to document policy-relevant derivative effects and to improve the understanding and implementation of structural models.

Suggested Citation

  • Manuel Arellano & Stéphane Bonhomme, 2017. "Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 471-496, September.
    • Handle: RePEc:anr:reveco:v:9:y:2017:p:471-496
    DOI: 10.1146/annurev-economics-063016-104346
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    7. 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.
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    More about this item

    Keywords

    dynamic models; structural economic models; panel data; unobserved heterogeneity;
    All these keywords.

    JEL classification:

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

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