摘要
研究目标:动态测度中国经济增长的长期趋势。研究方法:构建非齐头时变混频动态因子模型估计中国经济增长的长期趋势,采用时变方差分解测算各要素对经济增长长期趋势的贡献。研究发现:中国经济增长趋势早已于2007年就步入了下行区间,而在2016年后它的主体态势开始从持续下行转向渐趋稳定;人口红利和技术红利的协同消退主导了本轮经济增长的趋势下行,而资本深化的相对稳定则为中高速增长提供了趋势动能;目前经济增长趋势已正式步入劳动投入渐趋稳定、技术增长渐趋稳定和资本深化渐趋稳定的三重稳定阶段,这说明由新冠肺炎疫情等局部冲击引致的经济增长率下行仅是短期现象,并不会轻易破坏中高速阶段稳态的趋势基础。研究创新:将时变要素和非齐头数据纳入混频动态因子模型,提高了模型处理非规则数据的能力。研究价值:充分肯定了当下的经济波动更多是源于暂时性冲击,而中高速增长具备扎实的趋势基础。
Research Objectives: Measure the long-term trend of China’s economic growth dynamically. Research Methods: Based on the quarterly and monthly mix-frequency data from 1992 M1 to 2019 M12, this article constructs a time-varying mixed-frequency dynamic factor model with different starting points to measure the long-term trends of China’s economic growth, and then utilizes the time-varying decomposition to estimate the contribution of each production factor on the long-term trend. Research Findings: China’s economic growth trend has already entered a downward stage since 2007, and its main posture began to shift from a continuous downward trend to a relatively stable state after 2016. The coordinated decline of demographic dividends and technological dividends dominates the downward trend of this round of economic contraction, while the relative stability of capital deepening provides important support for the steady medium-to-high speed growth. China’s growth trend has officially entered a tripe stabilization stage of labor input, technological progress as well as capital deepening, which indicates the decline in economic growth rate caused by local shocks such as the epidemic is only a short-term phenomenon, but cannot easily break down the trend foundation of steady state in the medium-to-high speed stages. Research Innovations: By combining time-varying elements as well as the data with different starting points with traditional mixed-frequency dynamic factor model, this paper improves the model’s ability to handle irregular data. Research Value: Make a sufficient affirmation that the current fluctuations are temporary phenomenon, and China’s medium-to-high speed growth has a solid trend foundation.
作者
刘达禹
徐斌
王俏茹
Liu Dayu;Xu Bin;Wang Qiaoru(Center for Quantitative Economics,Jilin University;School of Economics and Management,Nanchang University)
出处
《数量经济技术经济研究》
CSSCI
CSCD
北大核心
2022年第7期26-46,共21页
Journal of Quantitative & Technological Economics
基金
国家社会科学基金后期资助项目:中国经济周期波动的基本态势、收敛特征与经济政策调控机制研究(20FJYB007)的资助。
关键词
经济增长
长期趋势
非齐头时变混频动态因子模型
要素分解
Economic Growth
Long-term Trend
Time-varying Mixed-frequency Dynamic Factor Model with Different Starting Points
Factor Decomposition