arXiv 13 Mar 2026 · Econometrics
arXiv:2603.13505 · PDF · DOI · OpenAlex · Extracted main text
Instrumental variable (IV) methods rely critically on the exclusion restriction, which is untestable in exactly-identified models under standard assumptions. We propose a framework combining IV analysis with the LiNGAM method to test this restriction by exploiting non-Gaussianity in the data. Under non-Gaussian structural errors, the exclusion violation parameter is point-identified without additional instruments. Five complementary tests (bootstrap percentile, asymptotic normal, permutation, likelihood ratio, and independence-based) are introduced to assess the restriction under varying data conditions. Monte Carlo simulations and an empirical application to the Card (1995) dataset demonstrate controlled Type I error rates and reasonable power against economically relevant violations.
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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 | Shimizu, S. and Hoyer, P. O. and Hyvärinen, A. and Kerminen, A. and… (2006) A linear non-Gaussian acyclic model for causal discovery | 1.000 | 6 | 3 | 100% |
| 2 | Shimizu, S. and Inazumi, T. and Sogawa, Y. and Hyvärinen, A. and oth… (2011) DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model | 0.737 | 3 | 2 | 100% |
| 3 | Shimizu, S (2019) Non-Gaussian methods for causal structure learning | 0.737 | 3 | 2 | 100% |
| 4 | Wang, Y. S. and Drton, M (2020) High-dimensional causal discovery under non-Gaussianity | 0.737 | 3 | 2 | 100% |
| 5 | Angrist, J. and Imbens, G (1995) Identification and estimation of local average treatment effects | 0.644 | 2 | 2 | 100% |
| 6 | Conley, Timothy G and Hansen, Christian B and Rossi, Peter E (2012) Plausibly exogenous | 0.644 | 2 | 2 | 100% |
| 7 | Dieterle, Steven G and Snell, Andy (2016) A simple diagnostic to investigate instrument validity and heterogeneous effects when using a single instrument | 0.644 | 2 | 2 | 100% |
| 8 | Kitagawa, Toru (2015) A test for instrument validity | 0.644 | 2 | 2 | 100% |
| 9 | Li, Chunxiao and Rudin, Cynthia and McCormick, Tyler H (2022) Rethinking nonlinear instrumental variable models through prediction validity | 0.644 | 2 | 2 | 100% |
| 10 | Mourifié, Ismaël and Wan, Yuanyuan (2017) Testing local average treatment effect assumptions | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 28 scored citations.