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Nonlinear Panel Data Estimation via Quantile Regression

Manuel Arellano () and Stéphane Bonhomme ()
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Stéphane Bonhomme: University of Chicago, http://www.uchicago.edu

Working Papers from CEMFI

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.

Keywords: Panel data; dynamic models; non-separable heterogeneity; quantile regression; expectation-maximization. (search for similar items in EconPapers)
JEL-codes: C23 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets
Date: 2015-07
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