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Nonlinear panel data estimation via quantile regressions

Authors: Manuel Arellano and St├ęphane Bonhomme
Date: 29 June 2016
Type: Journal article, Econometrics Journal,
DOI: 10.1111/ectj.12062


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.

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Previous version:
Manuel Arellano and St├ęphane Bonhomme July 2015, Nonlinear panel data estimation via quantile regressions, cemmap Working Paper, CWP40/15, Institute for Fiscal Studies

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