基于Lasso和K-means聚类的宏观经济周期阶段划分
Macroeconomic Cycle Stage Division Based on Lasso and K-means Clustering
李绮雯 1赵国瑞 2田婷婷 3曾烁飒 1邱福权2
作者信息
- 1. 广东海洋大学商学院,广东 阳江 529599
- 2. 广东海洋大学计算机科学与工程学院,广东 阳江 529599
- 3. 广东海洋大学材料科学与工程学院,广东 阳江 529599
- 折叠
摘要
经济周期划分对于把握市场经济规律本质具有重要意义.对经济周期划分的研究,国内外主要从经济学理论出发,对其质性进行研究.但影响经济周期划分的潜在因素繁多,且影响机制复杂,因此目前对经济周期划分的定量研究不足,尤其在经济学理论与机器学习等理论与方法的交叉研究方面有待加强.为此,从近20年的宏观经济数据出发,对高维宏观经济指标加入惩罚Lasso进行筛选,遴选出采购经理指数、居民消费指数等关键指标,继而进行K-means聚类,以对我国2001-2021年经济周期进行划分.
Abstract
The division of economic cycles is of great significance for grasping the essence of the laws of market economy.In this field,domestic and foreign research mainly focuses on qualitative research based on economic theory.However,there are many potential factors affecting the division of economic cycles,and the influencing mechanism is complex,so there are few quantitative studies,especially the interdisciplinary research with machine learning and other theories and methods needs to be strengthened.Based on the macroeconomic data of the past 20 years,this paper selects the high-dimensional macroeconomic indicators to add the penalty Lasso to the Key indicators,and then selects the purchasing managers'index and household consumption index as the Key indicators,and then performs K-means clustering to obtain the division of China's economic cycle from 2001 to 2021.
关键词
经济周期划分/机器学习/Lasso/K-means聚类Key words
economic cycle division/machine learning/Lasso/K-means clustering引用本文复制引用
出版年
2024
NETL
NSTL
万方数据