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Nonlinear panel data methods for dynamic heterogeneous agent models

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  • Manuel Arellano
  • Stéphane Bonhomme

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 e ffects, and to improve the understanding and facilitate the implementation of structural models.

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  • Manuel Arellano & Stéphane Bonhomme, 2016. "Nonlinear panel data methods for dynamic heterogeneous agent models," CeMMAP working papers 51/16, Institute for Fiscal Studies.
    • Handle: RePEc:azt:cemmap:51/16
    DOI: 10.1920/wp.cem.2016.5116
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