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Nonlinear panel data estimation via quantile regressions

Author

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  • Manuel Arellano

    () (Institute for Fiscal Studies and CEMFI)

  • Stéphane Bonhomme

    () (Institute for Fiscal Studies and University of Chicago)

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.

Suggested Citation

  • Manuel Arellano & Stéphane Bonhomme, 2015. "Nonlinear panel data estimation via quantile regressions," CeMMAP working papers CWP40/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:40/15
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    References listed on IDEAS

    as
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    Cited by:

    1. Galvao, Antonio F. & Kato, Kengo, 2016. "Smoothed quantile regression for panel data," Journal of Econometrics, Elsevier, vol. 193(1), pages 92-112.
    2. repec:eee:finsta:v:33:y:2017:i:c:p:331-345 is not listed on IDEAS

    More about this item

    Keywords

    Panel data; dynamic models; non-separable heterogeneity; quantile regression; expectation-maximization;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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