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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.
Authors
Research Fellow Centre for Monetary and Financial Studies (CEMFI)
Manuel is a Research Fellow of the IFS and a Professor of Econometrics at CEMFI, Madrid.
Professor of Economics University of Chicago
Working Paper details
- DOI
- 10.1920/wp.cem.2016.5116
- Publisher
- The IFS
Suggested citation
Arellano, M and Bonhomme, S. (2016). Nonlinear panel data methods for dynamic heterogeneous agent models. London: The IFS. Available at: https://ifs.org.uk/publications/nonlinear-panel-data-methods-dynamic-heterogeneous-agent-models (accessed: 20 April 2024).
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