Nonlinear panel data methods for dynamic heterogeneous agent models
Manuel Arellano () and
Stéphane Bonhomme ()
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Stéphane Bonhomme: Institute for Fiscal Studies and University of Chicago
No CWP51/16, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies
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 identifi cation 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.
Keywords: dynamic models; structural economic models; panel data; unobserved heterogeneity. (search for similar items in EconPapers)
JEL-codes: C23 (search for similar items in EconPapers)
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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)
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Persistent link: https://EconPapers.repec.org/RePEc:ifs:cemmap:51/16
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