Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/130054
Authors: 
Arellano, Manuel
Bonhomme, Stéphane
Year of Publication: 
2015
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP40/15
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.
Subjects: 
Panel data
dynamic models
non-separable heterogeneity
quantile regression
expectation-maximization
JEL: 
C23
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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