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Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models

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  • Manuel Arellano

    (CEMFI, Centro de Estudios Monetarios y Financieros)

  • Stéphane Bonhomme

    (University of Chicago)

Abstract

Recent developments in nonlinear panel data analysis allow identifying and estimating general dynamic systems. In this review we describe some results and techniques for nonparametric identification and flexible estimation in the presence of time-invariant and time-varying latent variables. This opens the possibility to estimate 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 facilitate the implementation of structural models.

Suggested Citation

  • Manuel Arellano & Stéphane Bonhomme, 2017. "Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models," Working Papers wp2017_1703, CEMFI.
    • Handle: RePEc:cmf:wpaper:wp2017_1703
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    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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