Journal Article

Nonlinear panel data estimation via quantile regressions

Authors

Manuel Arellano, Stéphane Bonhomme

Published Date

29 June 2016

Type

Journal Article

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.


Previous version

Nonlinear panel data estimation via quantile regressions
Manuel Arellano, Stéphane Bonhomme
CWP40/15