# Smoothed instrumental variables quantile regression

arXiv: https://arxiv.org/abs/2310.09013 · HTML: https://mlsanity.com/paper/2310.09013/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2023.

## Built on by (most cited first, 1 of 1)

- [Conditional Diffusion for Nonparametric Instrumental Variable Quantile Regression](https://mlsanity.com/paper/2608.08204/) (2026)

## Abstract (arXiv)

In this article, I introduce the sivqr command, which estimates the coefficients of the instrumental variables (IV) quantile regression model introduced by Chernozhukov and Hansen (2005). The sivqr command offers several advantages over the existing ivqreg and ivqreg2 commands for estimating this IV quantile regression model, which complements the alternative "triangular model" behind cqiv and the "local quantile treatment effect" model of ivqte. Computationally, sivqr implements the smoothed estimator of Kaplan and Sun (2017), who show that smoothing improves both computation time and statistical accuracy. Standard errors are computed analytically or by Bayesian bootstrap; for non-iid sampling, sivqr is compatible with bootstrap. I discuss syntax and the underlying methodology, and I compare sivqr with other commands in an example.