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Smoothed instrumental variables quantile regression

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  • David M. Kaplan

    (University of Missouri)

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.

Suggested Citation

  • David M. Kaplan, 2022. "Smoothed instrumental variables quantile regression," Stata Journal, StataCorp LLC, vol. 22(2), pages 379-403, June.
    • Handle: RePEc:tsj:stataj:v:22:y:2022:i:2:p:379-403
    DOI: 10.1177/1536867X221106404
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/st0676/
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