Nonlinear panel data estimation via quantile regressions

Manuel Arellano and Stéphane Bonhomme

Econometrics Journal, 2016, vol. 19, issue 3, C61-C94

Abstract: 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.

Date: 2016
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Citations: View citations in EconPapers (56)

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http://hdl.handle.net/10.1111/ectj.12062

Related works:
Working Paper: Nonlinear panel data estimation via quantile regressions (2015)
Working Paper: Nonlinear Panel Data Estimation via Quantile Regression (2015)
Working Paper: Nonlinear panel data estimation via quantile regressions (2015)
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