Nonlinear panel data estimation via quantile regressions

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 b...

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Bibliographic Details
Published in:The econometrics journal 2016-10, Vol.19 (3), p.C61-C94
Main Authors: Arellano, Manuel, Bonhomme, Stéphane
Format: Article
Language:English
Subjects:
Birth weight
Dynamic models
Econometrics
Economic theory
Expectation-maximization
Health behavior
Health economics
Non-separable heterogeneity
Panel data
Public health
Quantile regression
Smoking
Studies
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Summary: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.
ISSN:1368-4221
1368-423X
DOI:10.1111/ectj.12062