新常态条件下中国经济增长预测研究——基于货币政策调控视角

›› 2018, Vol. 30 ›› Issue (6): 28-39.

新常态条件下中国经济增长预测研究——基于货币政策调控视角

刘超^1,2, 蒋玉洁¹, 马玉洁¹, 周文文^1,2, 刘宸琦³

1. 北京工业大学经济与管理学院, 北京 100124;
2. 北京现代制造业发展研究基地, 北京 100124;
3. 迪金森学院, 卡莱尔 17013

收稿日期:2017-04-17 出版日期:2018-06-28 发布日期:2018-06-25
通讯作者: 周文文(通讯作者),北京工业大学经济与管理学院讲师
作者简介:刘超,北京工业大学经济与管理学院教授,博士生导师,博士;蒋玉洁,北京工业大学经济与管理学院硕士研究生;马玉洁,北京工业大学经济与管理学院博士研究生;刘宸琦,美国迪金森学院本科生。
基金资助:
国家自然科学基金项目（61773029；61273230；61603011；61603010；61703014）；北京市社科基金研究基地项目（16JDGLC005；17JDGLB019）；北京现代制造业发展研究基地；北京市属高校高水平教师队伍建设支持计划长城学者培养计划项目（CIT&TCD20170304）。

Forecast of China's Economic Growth under New Normal Conditions from the Perspective of Monetary Policy Regulation

Liu Chao^1,2, Jiang Yujie¹, Ma Yujie¹, Zhou Wenwen^1,2, Liu Chenqi³

1. School of Economics and Management, Beijing University of Technology, Beijing 100124;
2. Modern Manufacturing Industry Development Research Base of Beijing, Beijing 100124;
3. Dickinson College, Carlisle 17013

Received:2017-04-17 Online:2018-06-28 Published:2018-06-25

摘要/Abstract

摘要：

新常态条件下中国经济增长速度与质量的博弈是当前社会发展中的热点和难点问题，传统货币政策注重对经济增长速度的调控，经济发展新常态的提出为货币政策调控提出新的要求。本文从新常态条件下货币政策调控与经济增长速度和质量之间关系出发，选取1985-2016年货币政策调控及经济增长相关变量数据，将遗传算法的全局优化特性与BP神经网络的权值和阈值优化相结合构建货币政策调控与经济增长关系模型，模拟货币政策调控与新常态条件下经济增长速度和质量之间的交互行为，对2017年GDP增长率和三产贡献率进行预测分析，预测结果表明：2017年GDP增长率在6? 3389%-6? 6639%之间，经济增速进一步放缓；三产贡献率在52.2810%-54.9620%之间，经济增长质量增速显著，进一步研究发现自1985年以来三产贡献率不断提高，特别是2013年以后贡献率加快，第三产业对我国经济增长拉动作用不断增强，我国经济结构转型不断优化升级。

关键词: 经济新常态, 货币政策调控, 经济增长速度, 经济增长质量, 遗传算法优化的BP神经网络

Abstract:

The game between speed and quality of China's economic growth under the new normal conditions is a hotspot and difficult issue in the current social development. Traditional monetary policy focuses on the regulation of economic growth rate, but the proposed concept of the new normal of economic development puts forward new requirements for monetary policy regulation. This paper selects the monetary policy control and economic growth related variables data from 1985 to 2016 to research the relationship between the regulation of monetary policy and the speed and quality of economic growth under the new normal conditions. In this paper, the global optimization of genetic algorithms is combined with the weight and threshold optimization of BP neural networks to construct the relation model of monetary policy regulate and economic growth. This model simulates the interaction between monetary policy regulation and economic growth rate and quality under the new normal conditions. The GDP growth rate and the tertiary industry contribution rate of the forecast analysis results in 2017 showed by the model mean that the GDP growth of 2017 will slow down and be in the interval[6.3389%, 6.6639%] and the contribution of the tertiary industry will be in[52.2810%, 54.9620%]. Furthermore, the increase of contribution of the tertiary industry has accelerated since 1985, especially since 2013, which indicates that the third industry will play a major rule in economic development and the economic structure is being optimized.

Key words: the new normal conditions, monetary policy regulation, speed of economic growth, quality of economic growth BP neural network optimized by genetic algorithm

刘超, 蒋玉洁, 马玉洁, 周文文, 刘宸琦. 新常态条件下中国经济增长预测研究——基于货币政策调控视角[J]. , 2018, 30(6): 28-39.

Liu Chao, Jiang Yujie, Ma Yujie, Zhou Wenwen, Liu Chenqi. Forecast of China's Economic Growth under New Normal Conditions from the Perspective of Monetary Policy Regulation[J]. , 2018, 30(6): 28-39.

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