Serguei Maliar, Bernard Salanie
arXiv 28 Apr 2024 · Econometrics
arXiv:2404.18207 · PDF · DOI · OpenAlex · Extracted main text
The positive correlation test for asymmetric information developed by Chiappori and Salanie (2000) has been applied in many insurance markets. Most of the literature focuses on the special case of constant correlation; it also relies on restrictive parametric specifications for the choice of coverage and the occurrence of claims. We relax these restrictions by estimating conditional covariances and correlations using deep learning methods. We test the positive correlation property by using the intersection test of Chernozhukov, Lee, and Rosen (2013) and the "sorted groups" test of Chernozhukov, Demirer, Duflo, and Fernandez-Val (2023). Our results confirm earlier findings that the correlation between risk and coverage is small. Random forests and gradient boosting trees produce similar results to neural networks.
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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 | Chiappori and Salanié (2000) Testing for Asymmetric Information in Insurance Markets | 1.000 | 16 | 4 | 100% |
| 2 | Chernozhukov, Demirer, Duflo, and Fernández-Val (2023) Generic Machine Learning Inference On Heterogenous Treatment Effects in Randomized Experiments, With An Application To Immunizat… | 1.000 | 7 | 3 | 100% |
| 3 | Chernozhukov, Lee, and Rosen (2013) Intersection Bounds: Estimation and Inference | 0.928 | 4 | 3 | 100% |
| 4 | Semenova and Chernozhukov (2021) Debiased Machine Learning of Conditional Average Treatment Effects and Other Causal Functions | 0.811 | 4 | 2 | 100% |
| 5 | Chernozhukov et al (2018) Double/debiased machine learning for treatment and structural parameters | 0.737 | 3 | 2 | 100% |
| 6 | Chiappori, Jullien, Salanié, and Salanié (2006) Asymmetric information in insurance: general testable implications | 0.511 | 2 | 1 | 100% |
| 7 | Abadi et al (2016) TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems | 0.405 | 1 | 1 | 100% |
| 8 | Chollet (2021) Deep Learning with Python, Second Edition | 0.405 | 1 | 1 | 100% |
| 9 | Dionne, Gouriéroux, and Vanasse (2001) Testing for evidence of adverse selection in the automobile insurance market: A comment | 0.405 | 1 | 1 | 100% |
| 10 | Dionne, Gouriéroux, and Vanasse (2006) Informational content of household decisions with applications to insurance under asymmetric information | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 17 scored citations.