Nonparametric identification in panels using quantiles

A-Tier
Journal: Journal of Econometrics
Year: 2015
Volume: 188
Issue: 2
Pages: 378-392

Score contribution per author:

0.804 = (α=2.01 / 5 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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 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.

Technical Details

RePEc Handle
repec:eee:econom:v:188:y:2015:i:2:p:378-392
Journal Field
Econometrics
Author Count
5
Added to Database
2026-01-25