Binary Outcome Models with Extreme Covariates: Estimation and Prediction
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912241770561536 |
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| author | Liu, Laura Wang, Yulong |
| author_facet | Liu, Laura Wang, Yulong |
| contents | This paper presents a novel semiparametric method to study the effects of extreme events on binary outcomes and subsequently forecast future outcomes. Our approach, based on Bayes' theorem and regularly varying (RV) functions, facilitates a Pareto approximation in the tail without imposing parametric assumptions beyond the tail. We analyze cross-sectional as well as static and dynamic panel data models, incorporate additional covariates, and accommodate the unobserved unit-specific tail thickness and RV functions in panel data. We establish consistency and asymptotic normality of our tail estimator, and show that our objective function converges to that of a panel Logit regression on tail observations with the log extreme covariate as a regressor, thereby simplifying implementation. The empirical application assesses whether small banks become riskier when local housing prices sharply decline, a crucial channel in the 2007--2008 financial crisis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16041 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Binary Outcome Models with Extreme Covariates: Estimation and Prediction Liu, Laura Wang, Yulong Econometrics This paper presents a novel semiparametric method to study the effects of extreme events on binary outcomes and subsequently forecast future outcomes. Our approach, based on Bayes' theorem and regularly varying (RV) functions, facilitates a Pareto approximation in the tail without imposing parametric assumptions beyond the tail. We analyze cross-sectional as well as static and dynamic panel data models, incorporate additional covariates, and accommodate the unobserved unit-specific tail thickness and RV functions in panel data. We establish consistency and asymptotic normality of our tail estimator, and show that our objective function converges to that of a panel Logit regression on tail observations with the log extreme covariate as a regressor, thereby simplifying implementation. The empirical application assesses whether small banks become riskier when local housing prices sharply decline, a crucial channel in the 2007--2008 financial crisis. |
| title | Binary Outcome Models with Extreme Covariates: Estimation and Prediction |
| topic | Econometrics |
| url | https://arxiv.org/abs/2502.16041 |