Using SVM to Estimate and Predict Binary Choice Models
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911409284055040 |
|---|---|
| author | Chang, Yoosoon Park, Joon Y. Yan, Guo |
| author_facet | Chang, Yoosoon Park, Joon Y. Yan, Guo |
| contents | The support vector machine (SVM) has an asymptotic behavior that parallels that of the quasi-maximum likelihood estimator (QMLE) for binary outcomes generated by a binary choice model (BCM), although it is not a QMLE. We show that, under the linear conditional mean condition for covariates given the systematic component used in the QMLE slope consistency literature, the slope of the separating hyperplane given by the SVM consistently estimates the BCM slope parameter, as long as the class weight is used as required when binary outcomes are severely imbalanced. The SVM slope estimator is asymptotically equivalent to that of logistic regression in this sense. The finite-sample performance of the two estimators can be quite distinct depending on the distributions of covariates and errors, but neither dominates the other. The intercept parameter of the BCM can be consistently estimated once a consistent estimator of its slope parameter is obtained. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22659 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Using SVM to Estimate and Predict Binary Choice Models Chang, Yoosoon Park, Joon Y. Yan, Guo Econometrics The support vector machine (SVM) has an asymptotic behavior that parallels that of the quasi-maximum likelihood estimator (QMLE) for binary outcomes generated by a binary choice model (BCM), although it is not a QMLE. We show that, under the linear conditional mean condition for covariates given the systematic component used in the QMLE slope consistency literature, the slope of the separating hyperplane given by the SVM consistently estimates the BCM slope parameter, as long as the class weight is used as required when binary outcomes are severely imbalanced. The SVM slope estimator is asymptotically equivalent to that of logistic regression in this sense. The finite-sample performance of the two estimators can be quite distinct depending on the distributions of covariates and errors, but neither dominates the other. The intercept parameter of the BCM can be consistently estimated once a consistent estimator of its slope parameter is obtained. |
| title | Using SVM to Estimate and Predict Binary Choice Models |
| topic | Econometrics |
| url | https://arxiv.org/abs/2601.22659 |