Nonparametric identification in panels using quantiles

By Victor Chernozhukov, Iv'an Fern'andez-Val, Stefan Hoderlein, Hajo Holzmann, Whitney K. Newey. 2013

55 citations recorded by the source corpus.

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 inference for function-valued parameters such as quantile effects uniformly over a region of quantile indices and/or regressor values. An empirical application to Engel curve estimation with panel data illustrates the results.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/b755b59a27d105d96d98d43432e1cd65a9c8e0ac

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In Economics and Econometrics

Citations in the corpus

Common correlated effects estimation of heterogeneous dynamic panel quantile regression models (2020, unknown): “The recent literature include work by Koenker (2004), Lamarche (2010), Galvao (2011), Rosen (2012), Galvao, Lamarche and Lima (2013), Chernozhukov, Fernández-Val, Hahn and Newey (2013), Chernozhukov, Fernández-Val, Hoderlein, Holzmann and Newey (2015), Harding and Lamarche (2014, 2017), and Arellano”
A quantile correlated random coefficients panel data model (2016, background): “Since the movers’ UQE is identified, as well as Pr(X ∈ X) and Pr(X ∈ X) = 1− Pr(X ∈ X), the analog estimators for the lower and upper bounds of the identified set given in (21) are easy to compute.”
Nonlinear Panel Data Estimation via Quantile Regressions (2015, methodology): “Recent related approaches to quantile panel data models include Chernozhukov et al. (2013, 2015) and Graham et al. (2015).”