摘要
研究目标:我国宏观经济景气的实时监测与预测。研究方法:利用含约束条件的马尔科夫动态双因子模型进行先行、一致景气指数的提取以及宏观经济景气的监测与预测。研究发现:马尔科夫动态双因子模型可以准确识别宏观经济景气的阶段性变化。先行景气指数的转移动态以及预测能力表现出一定的区制依赖特征;景气指数转折点的实时识别存在一定的滞后性,而对未来6个月的景气实时预测具有相对较高的精度。研究创新:在对宏观经济景气预测时充分考虑了先行景气指数的前瞻式预警作用。研究价值:为我国经济景气状况的实时监测与预测提供了一个新的研究工具。
Research Objectives: Monitoring and forecasting China's macroeconomic conditions in real-time. Research Methods: LLsing markov dynamic bi-factor model with restriction to con-struct. new leading and coincident indexes,also using this model to monitor and forecast macro-economy in real time. Research Findings: Markov dynamic bi-factor model can identify the phases of macroeconomic condition accurately. The transformation dynamics and forecas-ting ability of the leading index show regime dependence characteristics. The identification of turning point using this index in real time is lagging behind,and the real-time forecasting of the economic index in the next 6 months has relatively high accuracy. Research Innovations : In the real-time forecast of macroeconomic conditions,we fully consider the early warning function of the forward-looking leading index. Research Value: It provides a new tool for re-al-time monitoring and forecasting of China's economic conditions.
作者
陈磊
孟勇刚
咸金坤
Chen Lei;Meng Yonggang;Xian Jinkun(School of Economics,Dongbei University of Finance and Economics;School of Public Economics and Administration. Shanghai University of Finance and Fxonomics)
出处
《数量经济技术经济研究》
CSSCI
CSCD
北大核心
2019年第2期86-102,共17页
Journal of Quantitative & Technological Economics
基金
国家社会科学基金重大项目"新常态下我国宏观经济监测和预测研究"(15ZDA011)的资助
关键词
马尔科夫区制转换
动态双因子模型
经济周期
实时
经济景气监测与预测
Markov Switching
Dynamic Bi-factor Model
Business Cycle
Real Time
Mo nitoring and ForecavSting of Macroeconomic Conditions
