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Nonparametric identification in panels using quantiles

Author

Listed:
  • 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)

  • Hajo Holzmann

    (Institute for Fiscal Studies)

  • Whitney K. Newey

    (Institute for Fiscal Studies and MIT)

Abstract
This paper considers identification 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 find that these derivatives are identified with two time periods for 'stayers', i.e. for individuals with the same regressor values in two time periods. We show that the identification results carry over to models that allow location and scale time effects. We propose nonparametric series methods and a weighted bootstrap scheme to estimate and make inference on the identified effects. The bootstrap proposed allows uniform inference for function-valued parameters such as quantile effects over a region of quantiles 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 & Hajo Holzmann & Whitney K. Newey, 2013. "Nonparametric identification in panels using quantiles," CeMMAP working papers CWP66/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:66/13
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    References listed on IDEAS

    as
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