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Nonlinear panel data estimation via quantile regressions

Listed author(s):
  • Manuel Arellano

    ()

    (Institute for Fiscal Studies and CEMFI)

  • Stéphane Bonhomme

    ()

    (Institute for Fiscal Studies and University of Chicago)

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.

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File URL: http://www.ifs.org.uk/uploads/cemmap/wps/cwp401515.pdf
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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP40/15.

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Date of creation: 15 Jul 2015
Handle: RePEc:ifs:cemmap:40/15
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  38. Victor Chernozhukov & Iván Fernández‐Val & Jinyong Hahn & Whitney Newey, 2013. "Average and Quantile Effects in Nonseparable Panel Models," Econometrica, Econometric Society, vol. 81(2), pages 535-580, March.
  39. Rosen, Adam M., 2012. "Set identification via quantile restrictions in short panels," Journal of Econometrics, Elsevier, vol. 166(1), pages 127-137.
  40. Manuel Arellano & Stèphane Bonhomme, 2011. "Nonlinear Panel Data Analysis," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 395-424, September.
  41. Lamarche, Carlos, 2010. "Robust penalized quantile regression estimation for panel data," Journal of Econometrics, Elsevier, vol. 157(2), pages 396-408, August.
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