Bootstrap Model Averaging

TL;DR

This paper introduces a bootstrap-based model averaging method that optimally combines models by minimizing a bootstrap criterion, achieving asymptotic optimality and improved performance over existing methods.

Contribution

It proposes a novel bootstrap model averaging approach with asymptotic optimality and convergence guarantees, enhancing model combination strategies.

Findings

Achieves asymptotic optimality in squared error loss

Convergence rate of bootstrap weights to optimal weights

Outperforms existing model averaging methods in simulations and applications

Abstract

Model averaging has gained significant attention in recent years due to its ability of fusing information from different models. The critical challenge in frequentist model averaging is the choice of weight vector. The bootstrap method, known for its favorable properties, presents a new solution. In this paper, we propose a bootstrap model averaging approach that selects the weights by minimizing a bootstrap criterion. Our weight selection criterion can also be interpreted as a bootstrap aggregating. We demonstrate that the resultant estimator is asymptotically optimal in the sense that it achieves the lowest possible squared error loss. Furthermore, we establish the convergence rate of bootstrap weights tending to the theoretically optimal weights. Additionally, we derive the limiting distribution for our proposed model averaging estimator. Through simulation studies and empirical applications, we show that our proposed method often has better performance than other commonly used model selection and model averaging methods, and bootstrap variants.