A Study on the Influence of the Shadow Banking System on the Selection ofthe Monetary Policy Tool Rule in China——Based on the DSGE Model of the Numerical Simulation Analysis

Abstract

Abstract: This Paper constructs the DSGE model containing multiple departments such as shadow banks and commercial banks, and makes use of different tools of monetary policy rule response coefficient estimation results of OLS, to explore the problem of choosing in the full development of the shadow banks under the background of the monetary policy tool rules. The model analysis results show that in terms of stable output and price, interest rate rule plays a greater role and should be regarded as the optimal choice of monetary policy tool rule in the future in China. Moreover, the comparison of the welfare loss in different monetary policy tool rules comes to the the same conclusion: the welfare loss of the three kinds of monetary policy tool rules increases from Interest rule to Mixing rule to Money supply rule, which also indicates that Interest rate rule is the optimal choice of China's monetary policy tool rule.

Key words: Shadow Banking System, Monetary Policy, Tool Rule, DSGE Model

摘要： 本文通过建立包含影子银行和商业银行等多个部门在内的DSGE模型,利用不同货币政策工具规则反应系数的OLS估计结果,探索解决在充分发展影子银行背景下的货币政策工具规则的选择问题。模型分析结果表明,利率规则在稳定产出、物价方面发挥的作用更明显,利率规则未来应作为我国货币政策工具规则的最优选择。同时,比较货币政策工具规则的福利损失得出结论,三种货币政策工具规则的福利损失从小到大分别是利率规则、混合规则和货币供应量规则,这也表明利率规则是我国货币政策工具规则的最优选择。

关键词: 影子银行体系, 货币政策, 工具规则, DSGE模型

CLC Number:

F822.1

LIU Cui. A Study on the Influence of the Shadow Banking System on the Selection ofthe Monetary Policy Tool Rule in China——Based on the DSGE Model of the Numerical Simulation Analysis[J]. , 2017, 33(8): 55-64.

刘翠. 影子银行体系对我国货币政策工具规则选择的影响——基于DSGE模型的数值模拟分析[J]. 财经论丛, 2017, 33(8): 55-64.

References

[1] McCulley, Paul. Teton Reflections, PIMCO Global Central Bank Focus[EB/OL]. http://www.pimco.com, 2007.
[2] Financial Stability Board. Shadow Banking: Scoping the Issues-A Background Note of the Financial Stability Board[Z].FSB Working Paper, 2011.
[3] Gennaioli N., Shleifer A., Vishny R. W.A Model of Shadow Banking[Z].NBER Working Paper, 2011.
[4] 周小川.金融政策对金融危机的响应——宏观审慎政策框架的形成背景、内在逻辑和主要内容[J].金融研究,2011,(1):1-14.
[5] 巴曙松.加强对影子银行系统的监管[J].中国金融,2009,(14):24-25.
[6] 周莉萍.影子银行体系的信用创造:机制、效应和应对思路[J].金融评论,2011,(4):37-53.
[7] 黄益平,常健,杨灵修.中国的影子银行会成为另一个次债？[J].国际经济评论,2012,(2):42-51.
[8] Gary B.Gorton, Andrew Metrick. Regulating the Bank Syetem[Z].Social Science Research Network,2010.
[9] Fabio Verona, Manuel M.F., Ines Drumond. (Un)Anticipated Monetary Policy in a DSGE Model with a Shadow Banking System[Z].Bank of Finland Research Discussion Paper, 2013.
[10] 李波,伍戈.影子银行的信用创造功能及其对货币政策的挑战[J].金融研究,2011,(12):77-84.
[11] 姚军,葛新峰.我国影子银行的发展现状及其对信贷调控政策的影响[J].金融纵横,2011,(10):11-14.
[12] 裘翔,周强龙.影子银行与货币政策传导[J].经济研究,2014,(5):91-105.
[13] 毛泽盛,许艳梅.影子银行、信贷渠道与货币政策非对称效应[J].财经论丛,2015,(3):39-47.
[14] 林琳,陈伟.影子银行背景下中国货币政策的实施效应[J].经济与管理研究,2015,(9):37-42.
[15] 史焕平,李泽成.货币政策、影子银行规模增速与经济增长[J].金融论坛,2015,(7):37-48.
[16] 李春吉,孟晓宇.中国经济波动——基于新凯恩斯主义垄断竞争模型的分析[J].经济研究,2006,(10):72-82.
[17] 贾丽平,郭薇.我国货币政策调控房地产价格的实证分析[J].统计与决策,2014,(8):124-127.
[18] 刘喜和,李良健,高明宽.不确定条件下我国货币政策工具规则稳健性比较研究[J].国际金融研究,2014,(7):7-17.
[19] 谢平,罗雄.泰勒规则及其在中国货币政策中的检验[J].经济研究,2002,(3):3-12.
[20] 杨英杰.泰勒规则与麦科勒姆规则在中国货币政策中的检验[J].数量经济技术经济研究,2002,(12):97-100.
[21] 奚君羊,贺云松.中国货币政策的福利损失及中介目标的选择——基于新凯恩斯DSGE模型的分析[J].财经研究,2010,(2):89-98.
[22] 鄢莉莉.金融中介效率对货币政策效果的影响——基于动态随机一般均衡模型的研究[J].国际金融研究,2012,(6):4-11.
[23] 李荣丽,徐龙滨,章上峰.存款利率市场化过程中货币政策工具的选择——基于动态随机一般均衡模型的模拟和分析[J].金融理论与实践,2014,(10):20-27.
[24] 黄广明.货币政策组合规则[J].经济学(季刊),2006,(1):479-495.
[25] Liu L., Zhang W. A Model Based Approach to Monetary Policy Analysis for China [Z].Hong kong Monetary Authority Working Paper, 2007.
[26] 张杰平.开放经济DSGE模型下我国货币政策规则的选择[J].山西财经大学学报,2012,(4):18-28.
[27] 刘斌.我国DSGE模型的开发及在货币政策分析中的应用[J].金融研究,2008,(10):1-20.
[28] 仝冰.货币、利率与资产价格——基于DSGE模型的分析与预测[D].北京:北京大学博士学位论文,2010.