Machine Learning in Econometric Models: Using SVM to Estimate and Predict Binary Choice Models by Guo Yan

Join us for this weeks Econometrics Research Seminar, Autumn Term 2024

Guo Yan, from the University of Melbourne, will present their research on Machine Learning in Econometric Models: Using SVM to Estimate and Predict Binary Choice Models.

Abstract

We study the use of support vector machines (SVM), a widely used machine learning classification method, for estimation and prediction in the context of binary choice models (BCM). We establish the asymptotic properties of both linear and kernel SVM, corresponding to BCMs with linear and nonparametric systematic components of covariates X, respectively. In the context of nonparametric BCM, the kernel SVM cannot be used for estimation, but it does provide an asymptotically optimal classification rule for prediction. In the context of a class of linear BCM, the linear SVM is optimal for prediction if and only if it can consistently estimate the linear coefficients. Although generally inconsistent, the linear SVM is √n consistent under certain conditions related to specific distributional aspects of the covariates and the error term. Under a symmetry condition on the distribution of X, we show that linear SVM can be more efficient than the most efficient estimator that does not exploit any distributional information about X. We provide parallel results for quasi-maximum likelihood estimators in BCMs using the same framework.

This seminar will be held on campus, is open to all levels of study and is also open to the public. To register your place and gain access to the webinar, please contact the seminar organisers.

This event is part of the Econometrics Research Seminar Series.