arXiv 23 Jan 2026 · Econometrics
arXiv:2601.16865 · PDF · DOI · OpenAlex · Extracted main text
We study instrumental-variable designs where policy reforms strongly shift the distribution of an endogenous variable but only weakly move its mean. We formalize this by introducing distributional relevance: instruments may be purely distributional. Within a triangular model, distributional relevance suffices for nonparametric identification of average structural effects via a control function. We then propose Quantile Least Squares (Q-LS), which aggregates conditional quantiles of X given Z into an optimal mean-square predictor and uses this projection as an instrument in a linear IV estimator. We establish consistency, asymptotic normality, and the validity of standard 2SLS variance formulas, and we discuss regularization across quantiles. Monte Carlo designs show that Q-LS delivers well-centered estimates and near-correct size when mean-based 2SLS suffers from weak instruments. In Health and Retirement Study data, Q-LS exploits Medicare Part D-induced distributional shifts in out-of-pocket risk to sharpen estimates of its effects on depression.
appendix boundary found by appendix_command · 70% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chernozhukov, Victor and Fernández-Val, Iván and Newey, Whitney and… (2020) Semiparametric estimation of structural functions in nonseparable triangular models | 0.836 | 12 | 4 | 58% |
| 2 | Andrews, Isaiah and Stock, James H and Sun, Liyang (2019) Weak instruments in instrumental variables regression: Theory and practice | 0.737 | 3 | 2 | 100% |
| 3 | Lewbel, Arthur (1997) Constructing instruments for regressions with measurement error when no additional data are available, with an application to pa… | 0.737 | 3 | 2 | 100% |
| 4 | Staiger, Douglas and Stock, James H (1997) Instrumental Variables Regression with Weak Instruments | 0.737 | 3 | 2 | 100% |
| 5 | Barcellos, Silvia Helena and Jacobson, Mireille (2015) The effects of Medicare on medical expenditure risk and financial strain | 0.644 | 4 | 2 | 50% |
| 6 | Tsyawo, Emmanuel Selorm (2022) Feasible IV regression without excluded instruments | 0.644 | 4 | 1 | 100% |
| 7 | Victor Chernozhukov and Iván Fernández-Val and Amanda E. Kowalski (2015) Quantile regression with censoring and endogeneity | 0.644 | 3 | 2 | 67% |
| 8 | Engelhardt, Gary V and Gruber, Jonathan (2011) Medicare Part D and the financial protection of the elderly | 0.644 | 3 | 2 | 67% |
| 9 | Scott, Kirstin Woody and Scott, John W and Sabbatini, Amber K and Ch… (2021) Assessing catastrophic health expenditures among uninsured people who seek care in US hospital-based emergency departments | 0.644 | 3 | 2 | 67% |
| 10 | Olea, José Luis Montiel and Pflueger, Carolin (2013) A robust test for weak instruments | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 49 scored citations.