Nonlinear panel data methods for dynamic heterogeneous agent models

Manuel Arellano and Stéphane Bonhomme

No 51/16, CeMMAP working papers from Institute for Fiscal Studies

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.

Date: 2016-11-01
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https://www.cemmap.ac.uk/wp-content/uploads/2020/08/CWP5116.pdf (application/pdf)

Related works:
Journal Article: Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models (2017)
Working Paper: Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models (2017)
Working Paper: Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models (2016)
Working Paper: Nonlinear panel data methods for dynamic heterogeneous agent models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:51/16

DOI: 10.1920/wp.cem.2016.5116

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