基地研究员胡淑兰教授与合作者（Renhe Wang, Zhongnan University of Economics and Law; Tong Wang, Business School, University of Edinburgh; Zhiyong Qian,Zhongnan University of Economics and Law）的论文 "A Bayesian estimation approach of random switching exponential smoothing with application to credit forecast" 在重要学术期刊Finance Research Letters在线发表。

Abstract:

      We introduce an efficient Markov Chain Monte Carlo sampler in precision-based algorithms for the estimation of the Random Switching Exponential Smoothing model, a versatile forecasting mechanism for time series data characterized with changing trends. Through a series of simulation experiments, RC-MCMC exhibits superior parameter estimation accuracy, particularly for datasets featuring low persistence trends. Furthermore, an empirical evaluation using the Bank for International Settlements’ quarterly time series data on the non-financial sector’s total credit relative to GDP validates the findings. The out-of-sample results indicate that the proposed approach outperforms its counterparts in estimating and forecasting accuracy for trending time series data.

       Keywords：Random switching exponential smoothing; Precision-based algorithms; Bayesian estimation; Forecasting; Credit

链接：https://doi.org/10.1016/j.frl.2023.104525

教师简介

胡淑兰，中南财经政法大学统计与数学学院教授，博士生导师，文澜青年学者，青年教师“科研新星”。中国现场统计学会资源与环境分会理事，大数据分会理事和湖北省现场统计学会理事。主要从事大数据统计算法理论及其应用、经济计量方法及金融应用等方面的研究。在 《Bernoulli》《Statistics Sinica》《Stochastic Processes and their applications》《Science in China》《中国科学》等国内外权威学术期刊发表论文二十余篇，独撰学术专著1本，主编“十四五”全国统计规划教材《经济计量学》。主持并完成国家自然科学基金青年项目、国家社会科学基金一般项目、中央高校课题、研究生精品课程建设、全英文课程建设项目、企事业单位横向课题等十几项。荣获全国市场调查大赛、全国统计案例大赛、全国工业与经济金融大数据建模与计算大赛、美国数学建模大赛等各类比赛一二等指导学生奖。