经济新常态多目标协调控制策略研究
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摘要
经济新常态多目标协调发展是现阶段社会经济健康平稳运行的重要保障。基于经济控制论的方法,探析经济新常态多目标之间的交互行为和耦合关系,运用Simulink模拟仿真平台构建经济新常态多目标系统仿真模型,通过检验模型的结构特性验证其有效性,并对关键变量进行调控,构建经济新常态多目标系统反馈控制模型,对经济新常态多目标协调发展的路径参数和实现途径进行模拟。结果表明,未来十年定期存款利率和中长期贷款利率均呈下降趋势且长期利差收窄;债券及股票融资规模的扩大、社会固定资产投资、R&D经费支出和国家财政社会保障与就业支出的增加将有力促进经济增长质量,为此,政府应持续加大投融资力度、科技创新投入和就业补助以实现四者快速增长;同时,还应将工业废气排放量以及工业废水排放量控制在一定的区间内。本文不仅为现阶段经济发展政策的实施提供理论支撑,还为经济新常态多目标的协调控制策略研究提供了方法指导。
The multi-objective coordination of China's economy under the New Normal condition is important to economic stability.Based on the economic cybernetics,this paper studies the interaction and coupling relationship among the multiple objects of China's economy under the New Normal condition. We employ the Simulink to establish a simulation model,and then adjust the key variables to structure the feedback model. Results demonstrate that the coordinated development paths of the multiple objects of the New Normal economy not only lie in the reduction of the range of benchmark rate,but also in connection with indirect financing scale,investment in fixed assets,R&D expenditure and financial employment expenditure. In order to improve the quality of economic growth,the government should increase investment expenditure,technology spending and employment subsidy constantly. At the same time,the amount of industrial waste gas emissions and industrial waste water emissions should be controlled within a certain range. This paper not only provides theoretical support for the implementation of economic policies,but also provides coordinated control strategies for economic development of the current new normal stage.
The multi-objective coordination of China's economy under the New Normal condition is important to economic stability.Based on the economic cybernetics,this paper studies the interaction and coupling relationship among the multiple objects of China's economy under the New Normal condition. We employ the Simulink to establish a simulation model,and then adjust the key variables to structure the feedback model. Results demonstrate that the coordinated development paths of the multiple objects of the New Normal economy not only lie in the reduction of the range of benchmark rate,but also in connection with indirect financing scale,investment in fixed assets,R&D expenditure and financial employment expenditure. In order to improve the quality of economic growth,the government should increase investment expenditure,technology spending and employment subsidy constantly. At the same time,the amount of industrial waste gas emissions and industrial waste water emissions should be controlled within a certain range. This paper not only provides theoretical support for the implementation of economic policies,but also provides coordinated control strategies for economic development of the current new normal stage.
引文
[1]李扬,张晓晶.“新常态”:经济发展的逻辑与前景[J].经济研究,2015,50(5):4-19
[2]郭克莎,汪红驹.经济新常态下宏观调控的若干重大转变[J].中国工业经济,2015,(11):5-15
[3]金碚.中国经济发展新常态研究[J].中国工业经济,2015,(1):5-18
[4]郝磊,张朝.新常态下中国宏观经济形势与对策选择[J].技术经济与管理研究,2016,(1):109-113
[5]潘敏.经济发展新常态下完善我国货币政策体系面临的挑战[J].金融研究,2016,(2):106-112
[6]张车伟.中国30年经济增长与就业:构建灵活安全的劳动力市场[J].中国工业经济,2009,(1):18-28
[7]王风云,王驰宇.经济危机对我国劳动力就业的影响分析[J].人口与经济,2010,(3):21-27
[8]卢锋,刘晓光,姜志霄,等.劳动力市场与中国宏观经济周期:兼谈奥肯定律在中国[J].中国社会科学,2015,(12):69-89
[9]何启志.经济新常态下的多层次资本市场建设[J].财贸研究,2016,27(4):95-100
[10]郭文旌,毛泽盛,刘敏楼,等.经济新常态与宏观金融调控国际研讨会综述[J].经济研究,2016,51(12):176-180
[11]范林凯,李晓萍,应珊珊.渐进式改革背景下产能过剩的现实基础与形成机理[J].中国工业经济,2015,(1):19-31
[12]冯文权.经济预测与决策技术.第4版[M].武汉:武汉大学出版社,2002
[13]龚德恩.经济控制论述评[J].中国人民大学学报,1988,2(6):45-54
[14]Nada T.,Viktorija P.Determination of the Economic Growth Rates by the Growth Quality[J].Economic Horizons,2014,16(2):125-135
[15]刘燕妮,安立仁,金田林.经济结构失衡背景下的中国经济增长质量[J].数量经济技术经济研究,2014,31(2):20-35
[16]Lee K.S.,Werner R.A.Reconsidering Monetary Policy:An Empirical Examination of the Relationship Between Interest Rates and Nominal GDP Growth in the U.S.,U.K.,Germany and Japan[J].Ecological Economics,2018,146:26-34
[17]Bekaert G.,Harvey C.,Lundblad C.Does Financial Liberalization Spur Growth?[J].Journal of Financial Economics,2005,77(1):3-55
[18]Smets F.,Wouter R.Shocks and Frictions in US Business Cycles:A Bayesian DSGE Approach[J].American Economic Review,2007,97(3):586-606
[19]Mertens K.Deposit Rate Ceilings and Monetary Transmission in the US[J].Journal of Monetary Economics,2008,55(7):1290-1302
[20]Akinboade O.A.Financial Development and Economic Growth[J].Savings And Development,1998,22(3):331-348
[21]金中夏,洪浩,李宏瑾.利率市场化对货币政策有效性和经济结构调整的影响[J].经济研究,2013,48(4):69-82
[22]Titman S.Financial Markets and Investment Externalities[J].Journal of Finance,2013,68(4):1307-1329
[23]陆蓉,何婧,崔晓蕾.资本市场错误定价与产业结构调整[J].经济研究,2017,52(11):104-118
[24]严武,刘斌斌.中小板上市企业股票融资的地区差异与产业结构升级研究[J].财贸经济,2012,(10):74-81
[25]杨长汉.信贷投放、固定资产投资与经济增长[J].宏观经济研究,2017,(5):21-28
[26]郝颖,辛清泉,刘星.地区差异、企业投资与经济增长质量[J].经济研究,2014,49(3):101-114
[27]张悦玫,张晓菡.我国财政支出研究的热点迁移趋势[J].财政研究,2017,(3):43-55
[28]王轩,刘骏民,王佳妮.我国社会保障评价体系重构、检验与分析[J].管理评论,2018,30(2):231-241
[29]尹音频,杨晓妹.劳动力市场中的财政政策效应---基于中国经验数据的实证分析[J].财经科学,2015,(7):60-69
[30]牟俊霖,王阳.财政政策、货币政策的就业效应研究---基于要素扩展的向量自回归模型的估计[J].宏观经济研究,2017,(3):16-31
[31]白俊红,王林东.创新驱动是否促进了经济增长质量的提升?[J].科学学研究,2016,34(11):1725-1735
[32]陈恒,侯建.R&D投入、FDI流入与国内创新能力的门槛效应研究---基于地区知识产权保护异质性视角[J].管理评论,2017,29(6):85-95
[33]Feldman M.P.The New Economics of Innovation,Spillovers and Agglomeration:A Review of Empirical Studies[J].Economics of Innovation&New Technology,1999,8(1-2):5-25
[34]Ghisetti C.,Pontoni F.Investigating Policy and R&D Effects on Environmental Innovation:A Meta-Analysis[J].Ecological Economics,2015,118(5):57-66
[35]Blazsek S.,Escribano A.Patent Propensity,R&D and Market Competition:Dynamic Spillovers of Innovation Leaders and Followers[J].Journal of Econometrics,2016,191(1):145-163
[36]李新功.政府R&D资助、金融信贷与企业技术创新[J].管理评论,2016,28(12):54-62
[37]Abdouli M.,Hammami S.Investigating the Causality Links between Environmental Quality,Foreign Direct Investment and Economic Growth in MENA Countries[J].International Business Review,2016,26(2):264-278
[38]Katrakilidis C.,Kyritsis I.,Patsika V.The Dynamic Linkages between Economic Growth,Environmental Quality and Health in Greece[J].Applied Economics Letters,2016,23(3):217-221
[39]王铭利.基于联立方程与状态空间模型对中国经济增长与环境污染关系的研究[J].管理评论,2016,28(7):75-84
[40]Lee S.,Wu S.C.A Multi-industry Bankruptcy Prediction Model Using Back-propagation Neural Network and Multivariate Discriminant Analysis[J].Expert Systems with Applications,2013,40(8):2941-2946
[41]Jobson J.D.A Multivariate Linear Regression Test for the Arbitrage Pricing Theory[J].Journal of Finance,2012,37(4):1037-1042
[42]胡泽文,武夷山.科技产出影响因素分析与预测研究---基于多元回归和BP神经网络的途径[J].科学学研究,2012,30(7):992-1004
[43]耿洁,路世昌.基于多元回归分析的我国能源消费量预测研究[J].资源开发与市场,2014,30(11):1317-1319
[44]钟洪燕.基于灰色系统理论的宏观经济运行机制及预测[J].统计与决策,2014,(1):145-148
[45]曾波,孟伟,刘思峰,等.面向灾害应急物资需求的灰色异构数据预测建模方法[J].中国管理科学,2015,23(8):84-91
[46]Javed S.A.,Liu S.Predicting the Research Output/Growth of Selected Countries:Application of Even GM(1,1)and NDGMModels[J].Scientometrics,2017,(3):1-19
[47]Akbilgic O.,Bozdogan H.,Balaban M.E.A Novel Hybrid RBF Neural Networks Model as a Forecaster[J].Statistics and Computing,2014,24(3):365-375
[48]Liu J.Prediction of Short Term Exchange Rate Using BP Neural Network[C].International Conference on Education Science and Economic Management,2017
[49]Fan Z.Q.,Wang G.,Ma J.An Improved Boosting Based on Feature Selection for Corporate Bankruptcy Prediction[J].Expert Systems with Applications,2014,41(5):2353-2361
[50]王泽旻,潘红.灰色系统模型在我国人口数量预测中的应用[J].统计与决策,2005,(1):19-19
[51]Kumar P.R.,Ravi V.Bankruptcy Prediction in Banks and Firms via Statistical and Intelligent Techniques---A Review[J].European Journal of Operational Research,2007,180(1):1-28
[52]王晶,王志胜,陆宁云,等.经济控制论---理论、应用与MATLAB仿真[M].北京:科学出版社,2008
[53]谢识予,朱弘鑫.高级计量经济学[M].上海:上海财经大学出版社,2005
[54]彭建刚,王舒军,关天宇.利率市场化导致商业银行利差缩窄吗?---来自中国银行业的经验证据[J].金融研究,2016,(7):48-63
(1)高投入、高消耗、高污染、低质量、低效益。
(2)格兰杰因果检验的前提是数据平稳,若存在非平稳的同阶单整序列,则需要进行协整检验以验证序列间的长期稳定关系,而后基于误差修正模型(VEC)进行格兰杰因果关系检验。
(3)表中“?→”表示原假设(箭头前的变量不是箭头后变量的格兰杰原因)及相应的P值,P值小于0. 05则说明拒绝原假设。同?理,“幈幇”中P值小于0. 05说明箭头两端变量互为格兰杰因果关系。?
(4)所构建的状态方程与输出方程经过统计方法检验。各个方程调整后的可决系数R2皆高于0.80,说明方程的拟合优度较高,且方程内各变量P值皆低于0.05,说明在5%的置信水平下都通过了显著性检验。
(5)表中加粗数字表示最小误差值。
(6)状态方程与输出方程调整后的可决系数高于0. 80,方程内各变量的P值小于0. 05,通过统计检验。
(7)表中加粗数字表示最小误差值。
[2]郭克莎,汪红驹.经济新常态下宏观调控的若干重大转变[J].中国工业经济,2015,(11):5-15
[3]金碚.中国经济发展新常态研究[J].中国工业经济,2015,(1):5-18
[4]郝磊,张朝.新常态下中国宏观经济形势与对策选择[J].技术经济与管理研究,2016,(1):109-113
[5]潘敏.经济发展新常态下完善我国货币政策体系面临的挑战[J].金融研究,2016,(2):106-112
[6]张车伟.中国30年经济增长与就业:构建灵活安全的劳动力市场[J].中国工业经济,2009,(1):18-28
[7]王风云,王驰宇.经济危机对我国劳动力就业的影响分析[J].人口与经济,2010,(3):21-27
[8]卢锋,刘晓光,姜志霄,等.劳动力市场与中国宏观经济周期:兼谈奥肯定律在中国[J].中国社会科学,2015,(12):69-89
[9]何启志.经济新常态下的多层次资本市场建设[J].财贸研究,2016,27(4):95-100
[10]郭文旌,毛泽盛,刘敏楼,等.经济新常态与宏观金融调控国际研讨会综述[J].经济研究,2016,51(12):176-180
[11]范林凯,李晓萍,应珊珊.渐进式改革背景下产能过剩的现实基础与形成机理[J].中国工业经济,2015,(1):19-31
[12]冯文权.经济预测与决策技术.第4版[M].武汉:武汉大学出版社,2002
[13]龚德恩.经济控制论述评[J].中国人民大学学报,1988,2(6):45-54
[14]Nada T.,Viktorija P.Determination of the Economic Growth Rates by the Growth Quality[J].Economic Horizons,2014,16(2):125-135
[15]刘燕妮,安立仁,金田林.经济结构失衡背景下的中国经济增长质量[J].数量经济技术经济研究,2014,31(2):20-35
[16]Lee K.S.,Werner R.A.Reconsidering Monetary Policy:An Empirical Examination of the Relationship Between Interest Rates and Nominal GDP Growth in the U.S.,U.K.,Germany and Japan[J].Ecological Economics,2018,146:26-34
[17]Bekaert G.,Harvey C.,Lundblad C.Does Financial Liberalization Spur Growth?[J].Journal of Financial Economics,2005,77(1):3-55
[18]Smets F.,Wouter R.Shocks and Frictions in US Business Cycles:A Bayesian DSGE Approach[J].American Economic Review,2007,97(3):586-606
[19]Mertens K.Deposit Rate Ceilings and Monetary Transmission in the US[J].Journal of Monetary Economics,2008,55(7):1290-1302
[20]Akinboade O.A.Financial Development and Economic Growth[J].Savings And Development,1998,22(3):331-348
[21]金中夏,洪浩,李宏瑾.利率市场化对货币政策有效性和经济结构调整的影响[J].经济研究,2013,48(4):69-82
[22]Titman S.Financial Markets and Investment Externalities[J].Journal of Finance,2013,68(4):1307-1329
[23]陆蓉,何婧,崔晓蕾.资本市场错误定价与产业结构调整[J].经济研究,2017,52(11):104-118
[24]严武,刘斌斌.中小板上市企业股票融资的地区差异与产业结构升级研究[J].财贸经济,2012,(10):74-81
[25]杨长汉.信贷投放、固定资产投资与经济增长[J].宏观经济研究,2017,(5):21-28
[26]郝颖,辛清泉,刘星.地区差异、企业投资与经济增长质量[J].经济研究,2014,49(3):101-114
[27]张悦玫,张晓菡.我国财政支出研究的热点迁移趋势[J].财政研究,2017,(3):43-55
[28]王轩,刘骏民,王佳妮.我国社会保障评价体系重构、检验与分析[J].管理评论,2018,30(2):231-241
[29]尹音频,杨晓妹.劳动力市场中的财政政策效应---基于中国经验数据的实证分析[J].财经科学,2015,(7):60-69
[30]牟俊霖,王阳.财政政策、货币政策的就业效应研究---基于要素扩展的向量自回归模型的估计[J].宏观经济研究,2017,(3):16-31
[31]白俊红,王林东.创新驱动是否促进了经济增长质量的提升?[J].科学学研究,2016,34(11):1725-1735
[32]陈恒,侯建.R&D投入、FDI流入与国内创新能力的门槛效应研究---基于地区知识产权保护异质性视角[J].管理评论,2017,29(6):85-95
[33]Feldman M.P.The New Economics of Innovation,Spillovers and Agglomeration:A Review of Empirical Studies[J].Economics of Innovation&New Technology,1999,8(1-2):5-25
[34]Ghisetti C.,Pontoni F.Investigating Policy and R&D Effects on Environmental Innovation:A Meta-Analysis[J].Ecological Economics,2015,118(5):57-66
[35]Blazsek S.,Escribano A.Patent Propensity,R&D and Market Competition:Dynamic Spillovers of Innovation Leaders and Followers[J].Journal of Econometrics,2016,191(1):145-163
[36]李新功.政府R&D资助、金融信贷与企业技术创新[J].管理评论,2016,28(12):54-62
[37]Abdouli M.,Hammami S.Investigating the Causality Links between Environmental Quality,Foreign Direct Investment and Economic Growth in MENA Countries[J].International Business Review,2016,26(2):264-278
[38]Katrakilidis C.,Kyritsis I.,Patsika V.The Dynamic Linkages between Economic Growth,Environmental Quality and Health in Greece[J].Applied Economics Letters,2016,23(3):217-221
[39]王铭利.基于联立方程与状态空间模型对中国经济增长与环境污染关系的研究[J].管理评论,2016,28(7):75-84
[40]Lee S.,Wu S.C.A Multi-industry Bankruptcy Prediction Model Using Back-propagation Neural Network and Multivariate Discriminant Analysis[J].Expert Systems with Applications,2013,40(8):2941-2946
[41]Jobson J.D.A Multivariate Linear Regression Test for the Arbitrage Pricing Theory[J].Journal of Finance,2012,37(4):1037-1042
[42]胡泽文,武夷山.科技产出影响因素分析与预测研究---基于多元回归和BP神经网络的途径[J].科学学研究,2012,30(7):992-1004
[43]耿洁,路世昌.基于多元回归分析的我国能源消费量预测研究[J].资源开发与市场,2014,30(11):1317-1319
[44]钟洪燕.基于灰色系统理论的宏观经济运行机制及预测[J].统计与决策,2014,(1):145-148
[45]曾波,孟伟,刘思峰,等.面向灾害应急物资需求的灰色异构数据预测建模方法[J].中国管理科学,2015,23(8):84-91
[46]Javed S.A.,Liu S.Predicting the Research Output/Growth of Selected Countries:Application of Even GM(1,1)and NDGMModels[J].Scientometrics,2017,(3):1-19
[47]Akbilgic O.,Bozdogan H.,Balaban M.E.A Novel Hybrid RBF Neural Networks Model as a Forecaster[J].Statistics and Computing,2014,24(3):365-375
[48]Liu J.Prediction of Short Term Exchange Rate Using BP Neural Network[C].International Conference on Education Science and Economic Management,2017
[49]Fan Z.Q.,Wang G.,Ma J.An Improved Boosting Based on Feature Selection for Corporate Bankruptcy Prediction[J].Expert Systems with Applications,2014,41(5):2353-2361
[50]王泽旻,潘红.灰色系统模型在我国人口数量预测中的应用[J].统计与决策,2005,(1):19-19
[51]Kumar P.R.,Ravi V.Bankruptcy Prediction in Banks and Firms via Statistical and Intelligent Techniques---A Review[J].European Journal of Operational Research,2007,180(1):1-28
[52]王晶,王志胜,陆宁云,等.经济控制论---理论、应用与MATLAB仿真[M].北京:科学出版社,2008
[53]谢识予,朱弘鑫.高级计量经济学[M].上海:上海财经大学出版社,2005
[54]彭建刚,王舒军,关天宇.利率市场化导致商业银行利差缩窄吗?---来自中国银行业的经验证据[J].金融研究,2016,(7):48-63
(1)高投入、高消耗、高污染、低质量、低效益。
(2)格兰杰因果检验的前提是数据平稳,若存在非平稳的同阶单整序列,则需要进行协整检验以验证序列间的长期稳定关系,而后基于误差修正模型(VEC)进行格兰杰因果关系检验。
(3)表中“?→”表示原假设(箭头前的变量不是箭头后变量的格兰杰原因)及相应的P值,P值小于0. 05则说明拒绝原假设。同?理,“幈幇”中P值小于0. 05说明箭头两端变量互为格兰杰因果关系。?
(4)所构建的状态方程与输出方程经过统计方法检验。各个方程调整后的可决系数R2皆高于0.80,说明方程的拟合优度较高,且方程内各变量P值皆低于0.05,说明在5%的置信水平下都通过了显著性检验。
(5)表中加粗数字表示最小误差值。
(6)状态方程与输出方程调整后的可决系数高于0. 80,方程内各变量的P值小于0. 05,通过统计检验。
(7)表中加粗数字表示最小误差值。