报告题目：Model Averaging for Decomposed Data
内容摘要：The decomposition-ensemble algorithm has received increasing attention in forecast and related fields, especially in capturing the nonlinear and nonstationary characteristics of time series data. A conventional strategy involves decomposing the target time series into various oscillation modes from the frequency domain and assigning equal weights to all decomposed modes for aggregated prediction. However, disparities in forecasting performance arise among different decomposed modes due to their distinct attributes and forecast horizons. This paper proposes a novel forward-validation model averaging approach to combine decomposed modes with appropriate weights, thereby enhancing the accuracy of the target time series forecast. It is shown that the proposed model averaging estimator is asymptotically optimal in the sense of achieving the lowest possible quadratic prediction risk. The rate of the selected weights converging to the optimal weights to minimizing the expected quadratic loss is established. Simulation studies and empirical applications to consumption and exchange rate forecasting highlight the merits of the proposed method.
主讲人简介：孙玉莹，中国科学院数学与系统科学研究院副研究员、博士生导师，国家优秀青年科学基金获得者，中国科协“青年人才托举工程”入选者。研究兴趣主要有计量经济学、经济预测理论与方法等。在Journal of Econometrics, European Journal of Operational Research等期刊上发表论文20余篇，30余篇政策研究报告和预测报告得到国家领导人批示或被中办、国办采用。先后获中国科学院数学与系统科学研究院“重要科研进展奖（2017，2019）”、陈景润未来之星、关肇直青年研究奖等。
报告时间：2024年04月02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：607 121 803
