Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models
Manuel Arellano () and
Stéphane Bonhomme ()
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Stéphane Bonhomme: Department of Economics, University of Chicago, Chicago, Illinois 60637
Annual Review of Economics, 2017, vol. 9, issue 1, 471-496
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.
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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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:anr:reveco:v:9:y:2017:p:471-496
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