Nonparametric identification in panels using quantiles
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 identif...
| Auteurs principaux: | , , , , |
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| Autres auteurs: | |
| Format: | Article |
| Publié: |
Elsevier BV
2018
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| Accès en ligne: | http://hdl.handle.net/1721.1/119462 https://orcid.org/0000-0002-3250-6714 https://orcid.org/0000-0003-2699-4704 |
| Résumé: | 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. Keywords: Panel data, nonseparable model, average effect, quantile effect, Engel curve |
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