报告题目：Time-Varying Model Averaging for GMM with Applications to Asset Pricing
内容摘要：This paper proposes a novel time-varying model averaging approach for generalized method of moments (GMM) to capture structural changes in economics and finance. Unlike existing literature, our approach allows for potential misspecification of moment conditions while permitting time-varying parameters and weights. The asymptotic optimality and convergence rate of the selected weights are derived and the consistency of the proposed averaging estimator is obtained. Moreover, we prove that if one or more candidate models contain correctly specified moment conditions, our method will asymptotically assign all the weights to them with probability approaching 1. Simulation studies demonstrate the superiority of our approach over competing methods. Applying our time-varying model averaging to stochastic discount factor models for pricing U.S. equity returns reduces model uncertainty by assigning time-varying weights to various instrumental variable-based moment conditions, constructing an investment strategy. Furthermore, the proposed method yields more profitable investment performance than other existing model selection techniques.
主讲人简介：现为中国科学院数学与系统科学研究院预测科学研究中心特别研究助理。2022年于湖南大学获得金融学博士学位。目前感兴趣的研究方向为广义矩估计、非参数估计、结构变化检验以及实证资产定价。
报告时间：2024年10月22日(周二)，16:30-18:00
线下地点：厦门大学经济楼N302
线上地点：腾讯会议 ID：346 351 092
