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Nonlinear panel data estimation via quantile regressions

Authors: Manuel Arellano and St├ęphane Bonhomme
Date: 15 July 2015
Type: cemmap Working Paper, CWP40/15
DOI: 10.1920/wp.cem.2015.4015

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 children’s birthweights completes the paper.

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Now published:
Manuel Arellano and St├ęphane Bonhomme June 2016, Nonlinear panel data estimation via quantile regressions, Journal Article, Wiley Online Library

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