Smoothed instrumental variables quantile regression

David Kaplan

Stata Journal, 2022, vol. 22, issue 2, 379-403

Abstract: In this article, I introduce the sivqr command, which estimates the coefficients of the instrumental variables quantile regression model introduced by Chernozhukov and Hansen (2005, Econometrica 73: 245–261). The sivqr com- mand offers several advantages over the existing ivqreg and ivqreg2 commands for estimating this instrumental variables quantile regression model, which com- plements the alternative “triangular model” behind cqiv and the “local quan- tile treatment effect” model of ivqte. Computationally, sivqr implements the smoothed estimator of Kaplan and Sun (2017, Econometric Theory 33: 105–157), who show that smoothing improves both computation time and statistical accu- racy. Standard errors are computed analytically or by Bayesian bootstrap; for nonindependent and identically distributed sampling, sivqr is compatible with bootstrap. I discuss syntax and the underlying methodology, and I compare sivqr with other commands in an example.

Keywords: sivqr; endogeneity; instrumental variables; quantile regression (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1177/1536867X221106404

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