Template-Type: ReDIF-Paper 1.0
Author-Name:  Javier Alejo
Author-Name-First: Javier
Author-Name-Last: Alejo
Author-Name:  Antonio F. Galvao
Author-Name-First:Antonio F.
Author-Name-Last: Galvao
Author-Name:  Gabriel Montes-Rojas
Author-Name-First: Gabriel
Author-Name-Last: Montes-Rojas
Title: A first-stage test for instrumental variables quantile regression.
Abstract:  This paper develops inference procedures to evaluate the validity of instruments in instrumental variables (IV) quantile regression (QR) models. We first derive a first-stage regression for the IVQR model, analogue to the least squares case, which is a weighted least-squares regression. The weights are given by the density function of the conditional distribution of the innovation term in the QR structural model, conditional on the exogenous covariates and the  nstruments. The first-stage regression is a natural framework to evaluate the instruments since we can test for their statistical significance. In the QR case, the instruments could be relevant at some quantiles but not for others or at the mean. Monte Carlo finite sample experiments show that the tests work as expected in terms of empirical size and power. Two applications illustrate that checking for the statistical significance of the instruments at di↵erent quantiles is important.
Length:  23 pages
Creation-Date:  2020-11
File-URL: https://aaep.org.ar/works/works2020/Alejo.pdf
File-Format: Application/pdf
Number: 4304
Classification-JEL: C13, C14, C21, C51, C53
Keywords:  quantile regression, instrumental variables, first-stage
Handle: RePEc:aep:anales:4304