Informativeness under Model Uncertainty: Shadow Prices and Ridge Penalties
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910138375340032 |
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| author | Lee, Jieun Maasoumi, Esfandiar |
| author_facet | Lee, Jieun Maasoumi, Esfandiar |
| contents | We develop inference under model uncertainty due to weak, noisy, multiple candidate restrictions and theories, and nuisance control covariates. A unified framework is given with degrees of misspecification and corresponding shadow prices, based on a Lagrangian constrained optimization approach, and a data$-$driven tolerance parameter selected via a Stein$-$type (shrinkage) risk criterion. A debiasing step is based on Karush$-$Kuhn$-$Tucker conditions. We introduce individual shadow prices (ISP) for different restrictions to measure empirical relevance and propose a plateau rule to separate signal from noise. We establish consistency and asymptotic normality of the estimators and characterize the ISP. Simulations and an application to a Solow growth model illustrate the method$^{\prime}$s practical usefulness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_15571 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Informativeness under Model Uncertainty: Shadow Prices and Ridge Penalties Lee, Jieun Maasoumi, Esfandiar Econometrics We develop inference under model uncertainty due to weak, noisy, multiple candidate restrictions and theories, and nuisance control covariates. A unified framework is given with degrees of misspecification and corresponding shadow prices, based on a Lagrangian constrained optimization approach, and a data$-$driven tolerance parameter selected via a Stein$-$type (shrinkage) risk criterion. A debiasing step is based on Karush$-$Kuhn$-$Tucker conditions. We introduce individual shadow prices (ISP) for different restrictions to measure empirical relevance and propose a plateau rule to separate signal from noise. We establish consistency and asymptotic normality of the estimators and characterize the ISP. Simulations and an application to a Solow growth model illustrate the method$^{\prime}$s practical usefulness. |
| title | Informativeness under Model Uncertainty: Shadow Prices and Ridge Penalties |
| topic | Econometrics |
| url | https://arxiv.org/abs/2604.15571 |