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Nonparametric identification in panels using quantiles

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  • Victor Chernozhukov

    () (Institute for Fiscal Studies and MIT)

  • Ivan Fernandez-Val

    (Institute for Fiscal Studies and Boston University)

  • Stefan Hoderlein

    () (Institute for Fiscal Studies and Boston College)

  • Whitney K. Newey

    () (Institute for Fiscal Studies and MIT)

Abstract

This paper considers identi?cation and estimation of ceteris paribus effects of continuous regressors in nonseparable panel models with time homogeneity. The effects of interest are derivatives of the average and quantile structural functions of the model. We ?nd that these derivatives are identi?ed with two time periods for “stayers”, i.e. for individuals with the same regressor values in two time periods. We show that the identi?cation results carry over to models that allow location and scale time e?ects. We propose nonparametric series methods and a weighted bootstrap scheme to estimate and make inference on the identi?ed e?ects. The bootstrap proposed allows inference for function-valued parameters such as quantile e?ects uniformly over a region of quantile indices and/or regressor values. An empirical application to Engel curve estimation with panel data illustrates the results.

Suggested Citation

  • Victor Chernozhukov & Ivan Fernandez-Val & Stefan Hoderlein & Whitney K. Newey, 2014. "Nonparametric identification in panels using quantiles," CeMMAP working papers CWP54/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:54/14
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Irene Botosaru & Chris Muris, 2017. "Binarization for panel models with fixed effects," CeMMAP working papers CWP31/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Manuel Arellano & Stéphane Bonhomme, 2015. "Nonlinear Panel Data Estimation via Quantile Regression," Working Papers wp2015_1505, CEMFI.
    3. Ghanem, Dalia, 2017. "Testing identifying assumptions in nonseparable panel data models," Journal of Econometrics, Elsevier, vol. 197(2), pages 202-217.
    4. repec:eee:econom:v:203:y:2018:i:1:p:113-128 is not listed on IDEAS
    5. Galvao, Antonio F. & Kato, Kengo, 2016. "Smoothed quantile regression for panel data," Journal of Econometrics, Elsevier, vol. 193(1), pages 92-112.
    6. Bryan S. Graham & Jinyong Hahn & Alexandre Poirier & James L. Powell, 2015. "Quantile Regression with Panel Data," NBER Working Papers 21034, National Bureau of Economic Research, Inc.
    7. Demian Pouzo & Zacharias Psaradakis & Martin Sola, 2016. "Maximum Likelihood Estimation in Possibly Misspecified Dynamic Models with Time-Inhomogeneous Markov Regimes," Papers 1612.04932, arXiv.org, revised May 2018.
    8. Stefan Hoderlein & Hajo Holzmann & Maximilian Kasy & Alexander Meister, 2015. "Erratum regarding “Instrumental variables with unrestricted heterogeneity and continuous treatment”," Boston College Working Papers in Economics 896, Boston College Department of Economics, revised 01 Feb 2016.
    9. Oliver Linton & Ji-Liang Shiu, 2018. "Semiparametric nonlinear panel data models with measurement error," CeMMAP working papers CWP09/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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    Keywords

    Panel data; nonseparable model; average e?ect; quantile e?ect; Engel curve;

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