基于混频数据模型的M0短期预测
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摘要
现阶段M0运行规律发生的深刻变化客观上对预测模型提出了更高要求。通过构建ADLMIDAS模型,从不同权重函数形式和预测方法的组合模型中得出最优预测模型,运用包含更多信息的混频数据对我国M0进行短期预测。预测结果显示,2018年M0增速将维持在5%以下,M0总量将处于7万亿—7.4万亿的区间,未来四年M0运行趋势依赖于宏观经济走势和非现金支付发展,M0增速和总量将在3%—8%以及7万亿—10万亿之间大幅波动。
At present, the changes of M0 operation rule have raised higher requirements on the prediction model. This paper employed ADL-MIDAS model, selected optimal forecasting model from different model combinations, and forecasting M0 in the short-term with more information of mixed frequency. The forecasting results show that M0 growth will be maintained below 5% in 2018, and the total M0 will be in the range of 7-7.4 trillion. The M0 operation trend in the next four years depends on the macro-economic cycle and non-cash payments, the M0 growth and total volume will fluctuate between 3%-8% and 7-10 trillion.
At present, the changes of M0 operation rule have raised higher requirements on the prediction model. This paper employed ADL-MIDAS model, selected optimal forecasting model from different model combinations, and forecasting M0 in the short-term with more information of mixed frequency. The forecasting results show that M0 growth will be maintained below 5% in 2018, and the total M0 will be in the range of 7-7.4 trillion. The M0 operation trend in the next four years depends on the macro-economic cycle and non-cash payments, the M0 growth and total volume will fluctuate between 3%-8% and 7-10 trillion.
引文
[1]陈彦斌,郭豫媚,陈伟泽. 2008年金融危机后中国货币数量论失效研究[J].经济研究,2015(4):21-35.
[2]何启志.货币和产出缺口能给通货膨胀提供有用的信息吗?[J].统计研究,2011(3):15-22.
[3]李斌.存差、金融控制与铸币税:兼对我国“M2/GDP过高之谜”的再解释[J].管理世界,2006(3):29-39.
[4]谢平,张怀清.融资结构、不良资产与中国M2/GDP[J].经济研究,2007(2):27-37.
[5]王磊,朱太辉.高货币化率的非货币化解释[J].国际金融研究,2016(12):13-22.
[6]赵文生,刘树林.货币理论重塑:分解与统一分析框架[J].财经研究,2017(9):4-20.
[7]王韧.经济开放、结构性冲击与货币供求机制变化[J].经济问题探索,2017(2):12-21.
[8]Gennaioli N, Shleifer A, Vishny R W. A Model of Shadow Banking[J]. The Journal of Finance, 2013, 68(4):1331-1363.
[9]孙国峰,贾君怡.中国影子银行界定及其规模测算:基于信用货币创造的视角[J].中国社会科学,2015(11):92-110.
[10]方先明,谢雨菲,权威.影子银行规模波动对金融稳定的溢出效应[J].经济学家,2017(1):79-87.
[11]Meeks R, Nelson B, Alessandri P. Shadow Banks and Macroeconomic Instability[J]. Journal of Money, Credit and Banking, 2017, 49(7):1483-1516.
[12]Freedman C. Monetary Policy Implementation:Past, Present and Future-will Electronic Money lead to the Eventual Demise of Central Banking?[J]. International Finance, 2000, 3(2):211-228.
[13]Owen A L, Fogelstrom C. Monetary Policy Implications of Electronic Currency:An Empirical Analysis[J]. Applied Economics Letters, 2005, 12(7):419-423.
[14]庄雷,赵成国.区块链技术创新下数字货币的演化研究:理论与框架[J].经济学家,2017(5):76-83.
[15]刘津含,陈建.数字货币对国际货币体系的影响研究[J].经济学家,2018(5):17-22.
[16]仝冰.中国M0与CPI的关系初探[J].金融理论与实践,2016(2):36-41.
[17]江春,李小林,张仓耀.中国货币供给变动与物价变动之间的关系:基于Morlet小波时频相关性分析[J].南方经济,2013(6):9-24.
[18]王亮,纪明明,张茜.电子货币、货币流通速度和货币政策有效性[J].金融理论与实践,2014(5):70-74.
[19]巫云仙,杨洁萌.中国货币流通中现金(M0)变化的经济学分析:基于互联网金融发展的背景[J].改革与战略,2016(5):77-81.
[20]郭菊娥.人民币现钞总量与结构需求预测[J].当代经济科学,2000(1):23-29.
[21]蔡晓春,邹克.货币供应量比率Mi/M0的时间序列分解分析及预测[J].统计与决策,2012(8):115-118.
[22]陈玉爽,李晓燕,吴雪纯.基于X-12季节调整法的货币供应量预测[J].产业与科技论坛,2018(3):45-46.
[23]Ghysels E, Santa-Clara P, Valkanov R. The MIDAS Touch:Mixed Data Sampling Regressions, University of North Carolina[R]. Mimeo, Chapel Hill, N.C,2004.
[24]Ghysels E, Santa-Clara P, Valkanov R. Predicting Volatility:Getting the most out of Return Data Sampled at Different Frequencies[J]. Journal of Econometrics, 2006, 131(1):59-95.
[25]Clements M P,Galv a?o A B.Macroeconomic Forecasting with Mixed-frequency Data:Forecasting Output Growth in the United States[J].Journal of Business&Economic Statistics,2008,26(4):546-554.
[26]Ghysels E,Ozkan N.Real-time Forecasting of the US Federal Government Budget:A Simple Mixed Frequency Data Regression Approach[J].International Journal of Forecasting,2015,31(4):1009-1020.
[27]王维国,于扬.基于混频回归类模型对中国季度GDP的预报方法研究[J].数量经济技术经济研究,2016(4):108-125.
[28]龚玉婷,陈强,郑旭.谁真正影响了股票和债券市场的相关性?--基于混频Copula模型的视角[J].经济学(季刊),2016(2):1205-1224.
[2]何启志.货币和产出缺口能给通货膨胀提供有用的信息吗?[J].统计研究,2011(3):15-22.
[3]李斌.存差、金融控制与铸币税:兼对我国“M2/GDP过高之谜”的再解释[J].管理世界,2006(3):29-39.
[4]谢平,张怀清.融资结构、不良资产与中国M2/GDP[J].经济研究,2007(2):27-37.
[5]王磊,朱太辉.高货币化率的非货币化解释[J].国际金融研究,2016(12):13-22.
[6]赵文生,刘树林.货币理论重塑:分解与统一分析框架[J].财经研究,2017(9):4-20.
[7]王韧.经济开放、结构性冲击与货币供求机制变化[J].经济问题探索,2017(2):12-21.
[8]Gennaioli N, Shleifer A, Vishny R W. A Model of Shadow Banking[J]. The Journal of Finance, 2013, 68(4):1331-1363.
[9]孙国峰,贾君怡.中国影子银行界定及其规模测算:基于信用货币创造的视角[J].中国社会科学,2015(11):92-110.
[10]方先明,谢雨菲,权威.影子银行规模波动对金融稳定的溢出效应[J].经济学家,2017(1):79-87.
[11]Meeks R, Nelson B, Alessandri P. Shadow Banks and Macroeconomic Instability[J]. Journal of Money, Credit and Banking, 2017, 49(7):1483-1516.
[12]Freedman C. Monetary Policy Implementation:Past, Present and Future-will Electronic Money lead to the Eventual Demise of Central Banking?[J]. International Finance, 2000, 3(2):211-228.
[13]Owen A L, Fogelstrom C. Monetary Policy Implications of Electronic Currency:An Empirical Analysis[J]. Applied Economics Letters, 2005, 12(7):419-423.
[14]庄雷,赵成国.区块链技术创新下数字货币的演化研究:理论与框架[J].经济学家,2017(5):76-83.
[15]刘津含,陈建.数字货币对国际货币体系的影响研究[J].经济学家,2018(5):17-22.
[16]仝冰.中国M0与CPI的关系初探[J].金融理论与实践,2016(2):36-41.
[17]江春,李小林,张仓耀.中国货币供给变动与物价变动之间的关系:基于Morlet小波时频相关性分析[J].南方经济,2013(6):9-24.
[18]王亮,纪明明,张茜.电子货币、货币流通速度和货币政策有效性[J].金融理论与实践,2014(5):70-74.
[19]巫云仙,杨洁萌.中国货币流通中现金(M0)变化的经济学分析:基于互联网金融发展的背景[J].改革与战略,2016(5):77-81.
[20]郭菊娥.人民币现钞总量与结构需求预测[J].当代经济科学,2000(1):23-29.
[21]蔡晓春,邹克.货币供应量比率Mi/M0的时间序列分解分析及预测[J].统计与决策,2012(8):115-118.
[22]陈玉爽,李晓燕,吴雪纯.基于X-12季节调整法的货币供应量预测[J].产业与科技论坛,2018(3):45-46.
[23]Ghysels E, Santa-Clara P, Valkanov R. The MIDAS Touch:Mixed Data Sampling Regressions, University of North Carolina[R]. Mimeo, Chapel Hill, N.C,2004.
[24]Ghysels E, Santa-Clara P, Valkanov R. Predicting Volatility:Getting the most out of Return Data Sampled at Different Frequencies[J]. Journal of Econometrics, 2006, 131(1):59-95.
[25]Clements M P,Galv a?o A B.Macroeconomic Forecasting with Mixed-frequency Data:Forecasting Output Growth in the United States[J].Journal of Business&Economic Statistics,2008,26(4):546-554.
[26]Ghysels E,Ozkan N.Real-time Forecasting of the US Federal Government Budget:A Simple Mixed Frequency Data Regression Approach[J].International Journal of Forecasting,2015,31(4):1009-1020.
[27]王维国,于扬.基于混频回归类模型对中国季度GDP的预报方法研究[J].数量经济技术经济研究,2016(4):108-125.
[28]龚玉婷,陈强,郑旭.谁真正影响了股票和债券市场的相关性?--基于混频Copula模型的视角[J].经济学(季刊),2016(2):1205-1224.