We introduce a class of quantile regression estimators for short panels. Our framework covers static and dynamic autoregressive models, models with general predetermined regressors and models with multiple individual effects. We use quantile regression as a flexible tool to model the relationships between outcomes, covariates and heterogeneity. We develop an iterative simulation-based approach for estimation, which exploits the computational simplicity of ordinary quantile regression in each iteration step. Finally, an application to measure the effect of smoking during pregnancy on birthweight completes the paper.
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
Journal article details
- DOI
- 10.1111/ectj.12062
- Publisher
- Wiley Online Library
- Issue
- June 2016
Suggested citation
Arellano, M and Bonhomme, S. (2016). 'Nonlinear panel data estimation via quantile regressions' (2016)
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