Model Averaging Prediction by K-Fold Cross-Validation by Chu-An Liu

Join us for this weeks Econometrics Research Seminar, Spring Term 2022

Chu-An Liu from the Institute of Economics, Academia Sinica will present their research on Model Averaging Prediction by K-Fold Cross-Validation

Abstract

This paper considers the model averaging prediction in a quasi-likelihood framework that allows for parameter uncertainty and model misspecification. We propose an averaging prediction that selects the data-driven weights by minimizing a K-fold cross-validation. We provide two theoretical justifications for the proposed method. First, when all candidate models are mis specified, we show that the proposed averaging prediction using K-fold cross-validation weights is asymptotically optimal in the sense of achieving the lowest possible prediction risk. Second, when the model set includes correctly specified models, we demonstrate that the proposed K-fold cross validation asymptotically assigns all weights to the correctly specified models. Monte Carlo simulations show that the proposed averaging prediction achieves lower empirical risk than other existing model averaging methods. As an empirical illustration, the proposed method is applied to credit card default prediction.  

This seminar will be held via webinar on Zoom at 4pm on Wednesday 2nd March. This event 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.