STR模型及我国货币政策传导非线性研究
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摘要
20世纪80年代以前,经典线性计量经济学占着绝对主流的地位,但在实际应用中,经典线性时间模型,如标准的线性时间序列模型(如AR(p)或ARMA(p,q)等)忽视模型结构变化等,导致这一类模型用于预测20世纪70年代石油危机的失败,以及无法解释1987年的“黑色星期一”现象,因为经济学家至今也没找到令人信服的引起股市崩盘的显著的信息变化。基于上述原因,传统线性时间序列计量建模受到越来越多的挑战。与此同时,上世纪60年代兴起的非线性科学,正在改变人们对现实世界的传统看法,受到了科学界的日益重视。随着计算技术的发展,为经济学者提供了越来越多的可供选择的非线性理论模型来刻画经济变量中的非线性关系。然而,不幸的是,将理论模型转化为可检验的非线性时间序列计量模型常常并不是一件容易的事情。而基于平滑转换回归(STR)模型有一套可操作的估计与假设检验程序,和可用于不同类型数据的非线性建模,具有丰富的经济学含义等优点,使其近年来成为研究经济变量中的非线性关系的首选模型。特别是货币政策传导机制问题的研究中,STR模型日益受到重视。
传统线性时间序列模型在研究货币政策传导机制问题中所隐含的假设是,经济主体对货币政策的反应是一致的,如在不同经济状态下,如高通胀状态与低通胀状态下,经济变量间具有相同的线性关系。然而,经济事实是,在不同的经济状态,微观主体行为的差异性很可能导致经济变量间的非线性关系,如在高通胀期,微观经济主体往往有更高的欲望调整现金持有量;而在低通胀时期,微观经济主体由于存在调整价格的成本,对前期通胀的反应往往不敏感。货币政策实践也发现,紧缩性货币政策能够有效地抑制经济过热,而扩张性货币政策在治理经济衰退中却显得无能为力。而传统线性时间序列模型(如AR(p)或ARMA(p,q))无法刻画上述经济变量运行所表现出的多样性和不对称性的特征。而我国货币政策传导机制研究的相关文献,绝大多数是基于线性时间序列模型研究的。在少量基于非线性时间序列模型研究的文献中,主要是采用马尔可夫机制转换模型。马尔可夫机制转换模型的状态变量是不可观测,对变量所处状态的推断需要很多信息,信息的失真可能导致不精确的结论,进一步,这种模型不能给出机制(regimes)转换的非线性形式,一般只能推断不同机制转换的概率,这种特征使其应用受到局限。而STR模型的状态变量是可观测的,且能刻画经济不同状态下非线性转换形式。上述特点使得STR模型在货币政策传导机制非线性效应研究中,有着广泛的应用前景。
然而,我国基于STR模型,特别是STR模型的拓展模型,对我国货币政策传导机制的相关研究,基本处于空白状态。本文讨论了STR模型的设定和估计等问题,并对我国货币政策传导机制的非线性问题进行了研究。相对现有研究文献,本文的创新与意义在于:(1)研究视角的创新。本文首次应用微观面板数据模型,对我国货币政策传导机制进行研究。现有我国货币政策传导机制研究中,基本上是应用宏观加总数据,从宏观上对我国货币政策有效性进行研究,然而,宏观数据模型存在无法看清货币政策传导的微观机制、研究样本量偏小,存在识别问题等缺陷,而基于微观面板数据模型可克服上述不足,本文从微观角度研究表明我国利率政策和信用政策的有效性。(2)研究方法的创新。考虑到货币政策变量的内生性问题,本文首次利用向量平滑自回归模型对我国宏观货币政策有效性问题进行研究,进一步,本文首次应用广义脉冲响应函数分析表明,随着信贷扩张与信贷紧缩的周期性变化,我国货币政策效果具有非对称性,如在短期,当信贷紧缩时,产出变化对利率变化冲击更敏感等。考虑到不同特征的微观主体对货币政策的反应很可能不一致,本文分别首次应用阈值面板数据模型和非线性光滑面板数据模型研究表明,国有控股企业与非国有控股企业、以及不同偿债能力、不同利润率的企业,对货币政策的反应是不一致的,如对于国有控股上市公司,货币政策资产价格传导渠道失效,对于非国有控股上市公司,货币政策资产价格传导渠道表现出明显的非线性效应;上市公司举债能力越强,货币政策传导信用渠道效应越弱;对于高利润和亏损公司,利率政策效应越强;而对于利润率一般公司,信用政策效应较强。(3)在模型的估计方法上,本论文大量应用模拟退火的优化算法。相对于现有相关文献中所用的高斯-牛顿迭代等估计方法,模拟退火算法由于搜索更细密、能更好克服局部最优解等优点,从而使本文的研究结论更可靠。(4)本文的研究结论具有更丰富的经济学和政策含义。本论文研究发现了在不同信贷状态下,货币政策效应的差异性,如在短期,在信贷紧缩状态时,货币供给变化冲击给产出变化带来同向的累积影响,而在信贷扩张状态,货币供给变化冲击给产出变化带来反相的累积影响;在调控高利润行业如房地产业的投资过热问题时,应该以利率政策为主导,信用政策为辅的策略等。不难看出,上述发现是基于非线性STR模型所产生的结论,若不然,我们不可能产生这种结论,从这个意义上说,本文具有显著的学术和应用意义。
Before 1980s, classical linear time series econometrics had the dominant place.However, in practical application, classical linear time series models, such as standard linear time series models (such as AR(p) / ARMA(p,q)) ignore structure change of the model, which leads to failure of forecasting 1970s, oil crisis and can not explain the phenomenon of“Black Monday”happened in 1987.And until now, economists can not find out significant information variation which led to breakdown of the stock markets. For above reasons, traditional linear time series modeling is faced to more and more challenges. At the same time, nonlinear science developed from 1960s is changing traditional view of people towards the world and more importance is attached to it by science circles. With the development of computer technology, more and more nonlinear theoretical models can be chosen to depict nonlinear relationship between variables studied by economists. Unfortunately, theoretical modeling often does not translate easily into a testable time series econometric model. Smooth Transition Regression or STR models have the following advantages which make Smooth Transition Regression or STR models a good first choice for time series modeling: Firstly, STR models possess a set of operable procedures of estimation and test. Secondly, STR models can be applied to different types of data. Finally, STR models possess rich economic sense. Especially, more and more importance is attached to STR models when studying the problem of monetary policy transmission mechanism.
When applied to study the problem of monetary policy transmission mechanism, traditional linear time series models are on the assumption which they implicit that micro individuals have the same reaction to monetary policy. For example, in different economic phases, such as in a high inflation phase and in a low inflation phase, there is the same linear relationship between the variables. However, the fact is, in different phases, different economic behavior of individuals may lead to nonlinear relationship between the economical variables. For example, in a high inflation phase, micro individuals always have strong desire to adjust their cash in hand, but in a low inflation phase, micro individuals may be insensitive to prophase inflation because of adjust cost. When carrying out monetary policy we also find out the contractive monetary policy can effectively keep down economic overheat, but expansionary monetary policy has no effect on controlling economic declining. However, traditional linear time series models (such as AR (p) / ARMA (p, q)) can not describe the properties of diversity and asymmetry of above economic behavior. But most studies about Chinese monetary policy transmission mechanism are made by applying linear time series models, or Markov Switching Regime model or MSR model is also adopted in few nonlinear time series studies on Chinese monetary policy transmission mechanism. In a MSR model, the transition variable is not observable and much information is needed when we decide in which phase the variable we study is, and wrong information may give wrong conclusions. Further more, generally a MSR model can not describe the type of nonlinear transition, and only can infer the probability of transition between different regimes, which limits the application of MSR models. But the transition variable in a STR model is observable, and STR models can mimic smooth transition between different behaviors. For above properties of STR models, they will be widely applied to the study on nonlinear effect of monetary policy transmission.
However, it is very scarce of applying STR models, especially extended STR models to the nonlinear studies of Chinese monetary policy transmission mechanism. This dissertation investigates specification、estimation and other aspects of different kinds of STR models, and has an empirical study on nonlinear effect of Chinese monetary policy transmission. Compared to existing literature, the innovative points and significance of this dissertation are as follows: Firstly, this dissertation has innovative angle of view of study. this dissertation firstly applies micro panel data to the studies of Chinese monetary policy transimisson.The existing literature study Chinese monetary policy transmission from macro angle of view by applying macro aggregate data.However,models which apply macro aggregate data have many disadvantages, such as obscuring micro mechanism of monetary policy transmission , and problems of scarcity of sample data and identification. But models which apply micro data can overcome above disadvantages. This dissertation shows the effectiveness of interest policy and credit policy from micro angle of view.Secondly, this dissertation has innovative methods of studies. For considering that monetary policy variables probably are endogenous variables, vector smooth transition autogressive regression model is firstly applied to the study of Chinese monetary policy transimissoin.Furthermore, the technique of generalized impulse response function analysis is firstly used and the results show that, with cyclical variation of credit, the effect of Chinese monetary policies has significant asymmetry. For example, in the short-run the change of product has more sensitivity to the change of interest rate as economy is situated in credit contraction. For considering that different micro individuals may have different reaction to monetary policy, threshold panel model and nonlinear smooth transition panel model are firstly used in this dissertation and the results show that the effect of monetary policy is not identical between state-owned-holding and non-state-owned-holding listed companies, and between listed companies which possess different payment capacity for debt and different profit rate. For example, asset price channel of monetary policy transmission is ineffective for state-owned-holding listed companies but shows nonlinear effect for non-state-owned-holding listed companies, and the effect of monetary policy transmission through credit channel gets weaker with the increase of payment capacity for debt of listed companies, and the effect of interest policy is stronger for high-profit and red companies but the effect of credit policy is stronger for ordinary-profit companies.Thirdly,on the estimation of the models ,the technique of simulated annealing is widely used in this dissertation. Compared to the techniques existing literature used, such as the principle of Gauss-Newton iteration, simulated annealing can search more closely and can avoid the problem of local optimal solution, so the results this dissertation concludes are more credible. Finally this dissertation has richer economic and policy sense. This dissertation finds out that the effect of monetary policy is different when economy is in different credit phases. For example, in the short-run, the positive shocks of change of money have a positve cumulative impact on output as economy is situated in credit expansion,but the positive shocks of change of money have a negative cumulative impact on output as economy is situated in credit contraction, and to control overinvestment problem of some high-profit industries, such as real estate industry, we can take interest policy as dominant policy, and credit policy as assistant policy.Obviously,above findings are derived from nonlinear STR models. Otherwise we can not get those findings. From this point, this dissertation has much significance of knowledge and application.
传统线性时间序列模型在研究货币政策传导机制问题中所隐含的假设是,经济主体对货币政策的反应是一致的,如在不同经济状态下,如高通胀状态与低通胀状态下,经济变量间具有相同的线性关系。然而,经济事实是,在不同的经济状态,微观主体行为的差异性很可能导致经济变量间的非线性关系,如在高通胀期,微观经济主体往往有更高的欲望调整现金持有量;而在低通胀时期,微观经济主体由于存在调整价格的成本,对前期通胀的反应往往不敏感。货币政策实践也发现,紧缩性货币政策能够有效地抑制经济过热,而扩张性货币政策在治理经济衰退中却显得无能为力。而传统线性时间序列模型(如AR(p)或ARMA(p,q))无法刻画上述经济变量运行所表现出的多样性和不对称性的特征。而我国货币政策传导机制研究的相关文献,绝大多数是基于线性时间序列模型研究的。在少量基于非线性时间序列模型研究的文献中,主要是采用马尔可夫机制转换模型。马尔可夫机制转换模型的状态变量是不可观测,对变量所处状态的推断需要很多信息,信息的失真可能导致不精确的结论,进一步,这种模型不能给出机制(regimes)转换的非线性形式,一般只能推断不同机制转换的概率,这种特征使其应用受到局限。而STR模型的状态变量是可观测的,且能刻画经济不同状态下非线性转换形式。上述特点使得STR模型在货币政策传导机制非线性效应研究中,有着广泛的应用前景。
然而,我国基于STR模型,特别是STR模型的拓展模型,对我国货币政策传导机制的相关研究,基本处于空白状态。本文讨论了STR模型的设定和估计等问题,并对我国货币政策传导机制的非线性问题进行了研究。相对现有研究文献,本文的创新与意义在于:(1)研究视角的创新。本文首次应用微观面板数据模型,对我国货币政策传导机制进行研究。现有我国货币政策传导机制研究中,基本上是应用宏观加总数据,从宏观上对我国货币政策有效性进行研究,然而,宏观数据模型存在无法看清货币政策传导的微观机制、研究样本量偏小,存在识别问题等缺陷,而基于微观面板数据模型可克服上述不足,本文从微观角度研究表明我国利率政策和信用政策的有效性。(2)研究方法的创新。考虑到货币政策变量的内生性问题,本文首次利用向量平滑自回归模型对我国宏观货币政策有效性问题进行研究,进一步,本文首次应用广义脉冲响应函数分析表明,随着信贷扩张与信贷紧缩的周期性变化,我国货币政策效果具有非对称性,如在短期,当信贷紧缩时,产出变化对利率变化冲击更敏感等。考虑到不同特征的微观主体对货币政策的反应很可能不一致,本文分别首次应用阈值面板数据模型和非线性光滑面板数据模型研究表明,国有控股企业与非国有控股企业、以及不同偿债能力、不同利润率的企业,对货币政策的反应是不一致的,如对于国有控股上市公司,货币政策资产价格传导渠道失效,对于非国有控股上市公司,货币政策资产价格传导渠道表现出明显的非线性效应;上市公司举债能力越强,货币政策传导信用渠道效应越弱;对于高利润和亏损公司,利率政策效应越强;而对于利润率一般公司,信用政策效应较强。(3)在模型的估计方法上,本论文大量应用模拟退火的优化算法。相对于现有相关文献中所用的高斯-牛顿迭代等估计方法,模拟退火算法由于搜索更细密、能更好克服局部最优解等优点,从而使本文的研究结论更可靠。(4)本文的研究结论具有更丰富的经济学和政策含义。本论文研究发现了在不同信贷状态下,货币政策效应的差异性,如在短期,在信贷紧缩状态时,货币供给变化冲击给产出变化带来同向的累积影响,而在信贷扩张状态,货币供给变化冲击给产出变化带来反相的累积影响;在调控高利润行业如房地产业的投资过热问题时,应该以利率政策为主导,信用政策为辅的策略等。不难看出,上述发现是基于非线性STR模型所产生的结论,若不然,我们不可能产生这种结论,从这个意义上说,本文具有显著的学术和应用意义。
Before 1980s, classical linear time series econometrics had the dominant place.However, in practical application, classical linear time series models, such as standard linear time series models (such as AR(p) / ARMA(p,q)) ignore structure change of the model, which leads to failure of forecasting 1970s, oil crisis and can not explain the phenomenon of“Black Monday”happened in 1987.And until now, economists can not find out significant information variation which led to breakdown of the stock markets. For above reasons, traditional linear time series modeling is faced to more and more challenges. At the same time, nonlinear science developed from 1960s is changing traditional view of people towards the world and more importance is attached to it by science circles. With the development of computer technology, more and more nonlinear theoretical models can be chosen to depict nonlinear relationship between variables studied by economists. Unfortunately, theoretical modeling often does not translate easily into a testable time series econometric model. Smooth Transition Regression or STR models have the following advantages which make Smooth Transition Regression or STR models a good first choice for time series modeling: Firstly, STR models possess a set of operable procedures of estimation and test. Secondly, STR models can be applied to different types of data. Finally, STR models possess rich economic sense. Especially, more and more importance is attached to STR models when studying the problem of monetary policy transmission mechanism.
When applied to study the problem of monetary policy transmission mechanism, traditional linear time series models are on the assumption which they implicit that micro individuals have the same reaction to monetary policy. For example, in different economic phases, such as in a high inflation phase and in a low inflation phase, there is the same linear relationship between the variables. However, the fact is, in different phases, different economic behavior of individuals may lead to nonlinear relationship between the economical variables. For example, in a high inflation phase, micro individuals always have strong desire to adjust their cash in hand, but in a low inflation phase, micro individuals may be insensitive to prophase inflation because of adjust cost. When carrying out monetary policy we also find out the contractive monetary policy can effectively keep down economic overheat, but expansionary monetary policy has no effect on controlling economic declining. However, traditional linear time series models (such as AR (p) / ARMA (p, q)) can not describe the properties of diversity and asymmetry of above economic behavior. But most studies about Chinese monetary policy transmission mechanism are made by applying linear time series models, or Markov Switching Regime model or MSR model is also adopted in few nonlinear time series studies on Chinese monetary policy transmission mechanism. In a MSR model, the transition variable is not observable and much information is needed when we decide in which phase the variable we study is, and wrong information may give wrong conclusions. Further more, generally a MSR model can not describe the type of nonlinear transition, and only can infer the probability of transition between different regimes, which limits the application of MSR models. But the transition variable in a STR model is observable, and STR models can mimic smooth transition between different behaviors. For above properties of STR models, they will be widely applied to the study on nonlinear effect of monetary policy transmission.
However, it is very scarce of applying STR models, especially extended STR models to the nonlinear studies of Chinese monetary policy transmission mechanism. This dissertation investigates specification、estimation and other aspects of different kinds of STR models, and has an empirical study on nonlinear effect of Chinese monetary policy transmission. Compared to existing literature, the innovative points and significance of this dissertation are as follows: Firstly, this dissertation has innovative angle of view of study. this dissertation firstly applies micro panel data to the studies of Chinese monetary policy transimisson.The existing literature study Chinese monetary policy transmission from macro angle of view by applying macro aggregate data.However,models which apply macro aggregate data have many disadvantages, such as obscuring micro mechanism of monetary policy transmission , and problems of scarcity of sample data and identification. But models which apply micro data can overcome above disadvantages. This dissertation shows the effectiveness of interest policy and credit policy from micro angle of view.Secondly, this dissertation has innovative methods of studies. For considering that monetary policy variables probably are endogenous variables, vector smooth transition autogressive regression model is firstly applied to the study of Chinese monetary policy transimissoin.Furthermore, the technique of generalized impulse response function analysis is firstly used and the results show that, with cyclical variation of credit, the effect of Chinese monetary policies has significant asymmetry. For example, in the short-run the change of product has more sensitivity to the change of interest rate as economy is situated in credit contraction. For considering that different micro individuals may have different reaction to monetary policy, threshold panel model and nonlinear smooth transition panel model are firstly used in this dissertation and the results show that the effect of monetary policy is not identical between state-owned-holding and non-state-owned-holding listed companies, and between listed companies which possess different payment capacity for debt and different profit rate. For example, asset price channel of monetary policy transmission is ineffective for state-owned-holding listed companies but shows nonlinear effect for non-state-owned-holding listed companies, and the effect of monetary policy transmission through credit channel gets weaker with the increase of payment capacity for debt of listed companies, and the effect of interest policy is stronger for high-profit and red companies but the effect of credit policy is stronger for ordinary-profit companies.Thirdly,on the estimation of the models ,the technique of simulated annealing is widely used in this dissertation. Compared to the techniques existing literature used, such as the principle of Gauss-Newton iteration, simulated annealing can search more closely and can avoid the problem of local optimal solution, so the results this dissertation concludes are more credible. Finally this dissertation has richer economic and policy sense. This dissertation finds out that the effect of monetary policy is different when economy is in different credit phases. For example, in the short-run, the positive shocks of change of money have a positve cumulative impact on output as economy is situated in credit expansion,but the positive shocks of change of money have a negative cumulative impact on output as economy is situated in credit contraction, and to control overinvestment problem of some high-profit industries, such as real estate industry, we can take interest policy as dominant policy, and credit policy as assistant policy.Obviously,above findings are derived from nonlinear STR models. Otherwise we can not get those findings. From this point, this dissertation has much significance of knowledge and application.
引文
[1] Abel, Eberly.Investment and q with fixed costs: an empirical analysis. Working paper, University of Pennsylvania, 1996.
[2] Angeloni, I., Anil, K., Benoit, M. and Daniele, T. Monetary transmission in the Euro Area: Where do we stand. ECB Working Paper, 2002, No. 114.
[3] Auerbach. Taxation, corporate financial policy and the cost of capital. Journal of Economic Literature, 1983, 21: 905~940.
[4] Bernanke, Ben S., Blinder, Alan S. Credit, money and aggregate demand. The American Economic Review, 1988, 78:435~39.
[5] Bernanke, Ben S., Gertler.Inside the black box: the credit channel of monetary policy transmission. Journal of Economic Perspectives, 1995, 12: 27~48.
[6] Bernanke, Gertler, and Gilchrist.The financial accelerator and the flight to quality. The Review of Economic Perspectives, 1996, 9:27~48.
[7] Bernanke, Blinder. The federal funds rate and the channels of monetary transmission. American Economic Review, 1992, 9: 901 ~921.
[8] Bond, Cummins. Noisy share prices and the Q model of investment. The Institute for Fiscal Studies, Working paper, 2001.
[9] Bond, Klemm, and Smith. The roles of expected profitability, Tobin Q and cash flow in econometric models of company investment. The Institute for Fiscal Studies, working paper, 2004.
[10] Brissimis, Kamberoglou, and Simigiannis.Is there a bank lending channel of monetary policy in Greece? Evidence from bank level data.ECB Working Paper, 2001, No. 104.
[11] Brüggemann, Lütkepohl. Lag selection in subset VAR models with an application to a U.S. monetary system. Econometric Studies, 2001, 107~128.
[12] Butzen, Fuss and Vermeulen.The interest rate and credit channel in Belgium: An investigation with micro-level firm data. ECB Working Paper, No.107, 2001.
[13] Carcia, C.J., Restrepo, J.E. Price inflation and exchange rate pass-through in Chile. Working Paper of Central Bank of Chile, No. 128, 2000.
[14] Carpenter, Guariglia, A. Cash flow, investment, and investment opportunities: New tests using UK panel data. Economic School of University of Nottingham, Discussion Paper, 2003.
[15] Carrasquilla.Monetary policy transmission: The Colombian case.BIS Policy Papers, No. 3, 1998, 81~104.
[16] Cleary .International Corporate Investment and the Relationships Between Financial Constraint Measures.Journal of Banking & Finance ,2006,30:1559–1580.
[17] Chan, Tong.On estimating thresholds in autoregressive models. Journal of Time Series Analysis, 1986, 7:179~194.
[18] Chan. Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model. Annals of Statistics, 1993, 21:521~533.
[19] Chatelain, Tiomo.Investment, the cost of capital and monetary policy in the nineties in France: A panel data investigation. ECB Working Paper, No. 106, 2001.
[20] Chatelain, Generale, Kalkreuth, and Vermeulen.Firm investment and monetary transmission in the Euro Area countries. Frankfurt: Deutsche Bundes Bank No.20, 2001.
[21] Chirinko.Business fixed investment spending: modeling strategies, empirical results, and policy implications. Journal of Economic Literature, 1993, 16: 1875 ~1911.
[22] Chirinko. Investment , Tobin’s Q and multiple capital inputs . NBER Working Paper, 1986.
[23] Cochrane. What does the VARs Mean? Measuring the output effects of monetary policy. Journal of Monetary Economics, 1998, 41: 277~ 300.
[24] Cont.Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 2001, 1: 223~236.
[25] Cummins, Jason, Kevin, Hassett and Steven .Investment behavior, observable expectations, and internal funds. Mimeograph, New York University, 1999.
[26] Cunningham, A. and Haldane.The monetary transmission mechanism in the United Kingdom: Pass-through and policy rules. Working Paper of Central Bank of Chile, No. 83, 2002.
[27] De la Roca, J.The transmission mechanism of monetary policy in Peru.BIS Policy Papers, No. 8, 1998, 181~194.
[28] Deck G,Scheuer T.Threshold accepting:A general purpose optimization algorithm appearing uperior to simulated annealing.Journal of Computational Physics,1990,90(1):161~175.
[29] Dewatripont, Mathias and Jean Tirole.A theory of debt and equity: Diversity of securities and manager-shareholder congruence. Quarterly Journal of Economics, 1994, 109:1027~1054.
[30] Dijk, Ter?svirta, and Franses.Smooth transition autoregressive models-A survey of recent developments. Econometric Reviews, 2002, 21: 1~47.
[31] Efron.Bootstrap methods: another look at the Jakknife.Annals of Statistics, 1979, 7:1~26.
[32] Eisner, R., Nadiri, M. Investment behavior and the Neo-Classical theory. The Review of Economics and Statistics, 1968, 50:369~382.
[33] Enders, Granger. Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business and Economics Statistics, 1998, 16:304~311.
[34] Engle. Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflations. Econometrica, 1982, 50: 987~1007.
[35] Engle. New frontiers for ARCH Models. Journal of Applied Econometrics, 2002,17:425~446.
[36] Fama.Efficient capital markets: A review of theory and empirical work. Journal of Finance, 1970, 2:383~417.
[37] Fama.The behavior of stock-market prices. The Journal of Business, 1965, 38:34~106.
[38] Farinha, Marques. The bank-lending channel of monetary policy: Identification and estimation using Portuguese micro bank data.ECB Working Paper, No. 102, 2001.
[39] Fazzari, Hubbard and Petersen. Financing constraints and corporate investment. Brooking Papers on Economic Activity, 1988, 1:141~195.
[40] Fazzari, Athey. Asymmetric information, financing constraints, and investments. Review of Economics and Statistics ,1987,69:481~487.
[41] Franses, Van and Dijk.Non-linear time series models in empirical finance. Cambridge University Press .Cambridge, 2000.
[42] Gaiotti, Generale. Does monetary policy have asymmetric effects? A look at the investment decisions of Italian firms.ECB Working Paper, No. 110, 2001.
[43] Gertler, Gilchrist.Monetary policy, business cycles, and the behavior of small manufacturing firms. Quarterly Journal of Economics, 1994, 30:309-340.
[44] Gianni. Financial constraints, the user cost of capital and corporate investment in Australia. Reserve Bank of Australia, Working Paper, 2004.
[45] Gilchrist, Himmelberg and Huberman.Do stock bubbles influence corporate investment. NBER Working Paper, 2004.
[46] Gilchrist, Himmelberg. Evidence on the role of cash flow for investment. Journal of Monetary Economics, 1995, 36:541~572.
[47] Goffe, Ferrier, and Rogers. Global optimization of statistical functions with simulated annealing. Journal of Econometrics, 1994, 60: 65~99.
[48] Gonzalez, Ter?svirta, and Dijk.Panel smooth transition regression models.University of Technology Sydney, Working Paper, 2005.
[49] Grabowski, Mueller. Managerial and stockholder welfare models of firm expenditures. The Review of Economics and Statistics, 1972, 54: 9 ~ 24.
[50] Granger. Overview of nonlinear macroeconometric models. Macroeconomic Dynamics, 2001, 5: 466~481.
[51] Granger,Ter?svirta.Modelling nonlinear economic relationships. Oxford University Press. Oxford, 1993.
[52] Grassberger, Procaccia.Characterization of strange attractors. Physical Review Letters, 1983, 50:346~349.
[53] Gugler, K. , Mueller , D. C. and Yurtoglu , B. Marginal q , average q , cash flow and investment .Southern Economic Journal , 2003 , 70: 512~531.
[54] Hamilton. A parametric approach to flexible nonlinear inference .Econometrica, 2001, 69:537~573.
[55] Hamilton. A new approach to the economic analysis of nonstationary time series and the business cycle.Econometrica, 1989, 57:357~384.
[56] Hamilton. Time series analysis. Princeton University Press .Princeton, 1994.
[57] Hansen. Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics, 1999, 93:345~368.
[58] Hansen. Inference when a nuisance parameter is not identified under the null hypothesis.Econometrica, 1996, 64:413~430.
[59] Hay, D. A., Morris, D. J. Industrial economics and organization. Oxford University Press. Oxford, 1991.
[60] Hayashi, F. Tobin’s marginal and average q: A neoclassical interpretation. Econometrica. 1982, 50:213~224.
[61] Hayashi.The cost of capital, Q, and the theory of investment in econometrics and the cost of capital.The MIT Press. Cambridge, 2000.
[62] Hernando, Martinez. Is there a bank-lending channel of monetary policy in Spain?ECB Working Paper, No. 99, 2001.
[63] Hsiao. Autoregressive modeling and money-income causality detection. Journal of Monetary Economics, 1981, 7:85~106.
[64] Hsieh, D. Chaos and nonlinear dynamics: Application to financial markets. Journal of Finance, 1991, 5: 1839~1877.
[65] Huh.Estimating asymmetric output cost of lowering inflation for Australia. Southern Economic Journal, 2002, 68:600~616.
[66] Johansen.Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 1991, 59:1551~1580.
[67] Jorgenson, D. Capital theory and investment behavior .American Economic Review, 1963, 53: 247~259.
[68] Kaplan, Zirgales.Do financing constraints explain why investment is correlated with cash flow.NBER Working Paper, 1995.
[69] Kasuya. Regime-switching approach to monetary policy effects: Empirical studies using a smooth transition vector autoregressive model. Bank of Japan, Working Paper, 2003.
[70] Kazuo Ogawa.Monetary transmission and inventory: Evidence from Japanese balance-sheet data by firm size. The Japanese Economic Review, 2002, 53:425 ~443.
[71] Kim, Osborn and Sensier. Nonlinearity in the Fed`s monetary policy rule. CGBCR Discussion Paper ,018, 2002.
[72] Kirkpatrieks,Gelatt,and Vecch.Optimization by simulated annealing Science, 1983, 220:671~680.
[73] Koop, Pesaran and Potter. Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 1996, 74:119~147.
[74] Krolzig H.M. Markov-switching vector autoregressions: modeling, statistical inference and application to business cycle analysis. Journal of Econometrics,1997, 65:102~135.
[75] Laarhoven P , Aans E.Simulated annealing:Theory and applications.D.Reidel Publishing Company.Dordrecht, 1987.
[76] Lundbergh, Ter?svirta and Van Dijk.Time-varying smooth transition autoregressive models. Stockholm School of Economics, Working Paper Series in Economics and Finance NO.376, 2000.
[77] Loayza, Schmidt-Hebbel, K. Monetary policy functions and transmission mechanisms: An overview.Central Bank of Chile, 2002.
[78] Luukkonen, Saikkonen and Ter?svirtatesting .Linearity against smooth transition autoregressive models.Biometrika, 1988, 75, 3:491~499.
[79] Lyziak, T. Monetary transmission mechanism in Poland: Theoretical concepts vs. evidence. The National Bank of Poland, Working, Paper No. 19, 2001.
[80] Martinez, Sanchez, O. and Werner, A. Monetary policy and the transmission mechanism in Mexico. Papers Presented at Banco de México's 75th Anniversary Seminar Mexico City November 14-15, 2000, Banco de México, 197~261.
[81] McKinnon. Money and capital in economic development. The Brooking Institution, Washington DC, 1973.
[82] Michael, Henderson. STAR modeling for stocks and currencies. Global Investor, 1995 8:42~45.
[83] Misaico, Z.Q. Transmission mechanisms of monetary policy in an economy with partial dollarisation: the case of Peru.BIS Papers, No. 8, 2001.
[84] Mishkin, F. S. The economics of money, banking and financial markets. Wesley. New York,2003,123~127.
[85] Metropolis N.Equations of state calculations by fast computing machines.Journal Chem Phys,1953,21:1 087~1 091.
[86] Nychka, Ellner, Gallant and McCaffrey. Finding chaos in noisy systems. Journal of the Royal Statistical Society, 1992, 54:399~426.
[87] Omar. Investigating the differential impact of real interest rates and credit availability on private investment: Evidence from Venezuela. Dissertation Texas A&M University, 2001.
[88] Paul, Simpson, Denise, Osborn and Marianne Sensier.Modeling business cycle movements in the UK economy.Economica, 2001, 68:243~267.
[89] Pesaran, M., Potter, S. A floor and ceiling model of US output. Journal of Economic Dynamics and Control, 1997, 21:661~695.
[90] Pesaran M., Shin Y. Generalized impulse response analysis in linear multivariate models. Economic Letters, 1998, 58: 17~29.
[91] Sarantis.Nonlinearities, cyclical behavior and predictability in stock markets: International evidence. International Journal of Forecasting, 2001 17:459~482.
[92] Sensier, Osborn and Ocal. Asymmetric interest rate effects for the UK real economy. Oxford Bulletin of Economics and Statistics, 2002, 64:315~339.
[93] Shaw.Fiancial deepening in economic development. Oxford University Press. New York, 1973.
[94] Sims, Christopher. Comparison of interwar and postwar business cycles: Monetarism reconsidered. American Economic Review, 1980, 70.
[95] Sims. Interpreting the macroeconomic time series facts: the effects of monetary policy. European Economic Review, 1992, 36:975~1011.
[96] Stulz.Managerial discretion and optimal financing choices. Journal of Financial Economics, 1990, 26:3~28.
[97] Summers. Does the stock market rationally reflect fundamental values. Journal of Finance 1986, 41: 591 ~600.
[98] Swanson. Money and output viewed through a rolling window. Journal of Monetary Economics, 1998, 41: 455~ 473.
[99] Szu H,Hartley P.Fast simulated annealing.Physics Letter A,1987,122:157~162.
[100] Takashi Nagahata, Toshitaka Sekine.Firm investment, monetary transmission andbalance-sheet problems in Japan: an investigation using micro data. Japan and World Economy, 2005, 17:345~369.
[101] Ter?svirta, Anderson. Characterizing nonlinearities in business cycles using smooth transition autoregressive models. Journal of Applied Econometrics, 1992, 7:119~136.
[102] Ter?svirta.Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 1994, 89: 208~218.
[103] Tobin, J. A general equilibrium approach to monetary theory .Journal of Money, Credit and Banking. 1969, 1:15~29.
[104] Tong, Howell. On a threshold model. Pattern Recognition and Signal Processing, 1978, 100-141.
[105] Tong, Howell .Non-linear time series: a dynamical system approach. Oxford University Press. Oxford, 1990.
[106] Tsay. Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 1998, 93:1188~1202.
[107] Tyssedal and Tj?stheim.An autoregressive model with suddenly changing parameters. Applied Statistics, 1988, 37:353~369.
[108] Valderrama, M.Credit channel and investment behavior in Austria: A micro -econometric approach.ECB Working Paper, No. 108, 2001.
[109] Von Kalckreuth, U.Monetary transmission in Germany: New perspectives on finance constraints and investment spending.ECB Working Paper, No. 109, 2001.
[110] Weise.The asymmetric effects of monetary policy: A nonlinear vector autoregression approach. Journal of Money, Credit, and Banking, 1999.
[111] W.Enders,S.Hurn.Asymmetric price adjustment and the Phillips Curve. Journal of Macroeconomics,2002, 24:395~412
[112] Worms, A. The reaction of bank lending to monetary policy measures inGermany.ECB Working Paper, No. 96, 2001.
[113]北京大学中国经济研究中心宏观组.产权约束、投资低效与通货紧缩.经济研究,2004,9:10~18.
[114]程建胜.“九五”时期中国货币政策评述.海南金融,2002,2:23~28.
[115]陈飞,赵昕东,高铁梅.我国货币政策工具变量效应的实证分析.金融研究, 2002, 10:25~30.
[116]高铁梅.计量经济分析方法与建模:Eviews应用及实例.北京:清华大学出版社,2005.128~131.
[117]龚江辉.企业投资决定理论的新发展.华东经济管理,2006,6:89~91.
[118]郭明星,刘金全,刘志刚.我国货币政策供给增长率与国内产出增长率之间的影响关系检验——来自MS-VECM模型的新证据.数量经济技术经济研究, 2005, 5:16~23.
[119]蒋瑛琨,刘艳武,赵振全.货币渠道与信贷渠道传导机制有效性的实证分析:兼论货币政策中介目标的选择.金融研究, 2005,5:70~79.
[120]刘斌.货币政策冲击的识别及我国货币政策有效性的实证研究.金融研究,2001,7:36~43.
[121]刘明.信贷配给与货币政策效果非对称性及“阈值效应”分析.金融研究, 2006, 2:18~26.
[122]彭方平,王少平.我国利率政策的微观效应.管理世界,2007,1:45～52.
[123]孙明华.我国货币政策传导机制的实证分析.财经研究,2004,3:42~46.
[124]孙华妤,马跃.中国货币市场与股票市场的关系.经济研究, 2003,7:17~23.
[125]唐齐鸣,王海生.中国货币政策的回顾与展望.华中理工大学学报,1997,1:23~27.
[126]童盼,陆正飞.负债融资、负债来源与企业投资行为.经济研究,2005,5:42~49.
[127]汪辉.上市公司债务融资、公司治理与市场价值.经济研究,2003,8:35~42.
[128]王少平,李子奈.我国货币需求的协整分析及其政策建议.经济研究,2004,7: 13~19.
[129]王少平,彭方平.我国通货膨胀与通货紧缩的非线性转换.经济研究,2006,8:35～42.
[130]王宇.伯南克时代:格林斯潘之后的美联储货币政策展望.大连:东北财经大学,2006.
[131]王新洲.非线性模型参数估计理论与应用.武汉:武汉大学出版社,2002.
[132]伍旭川,谢云山.“十五”货币政策回顾及展望.银行家,2006,3:17~21.
[133]徐亚平.货币政策有效性与货币政策透明制度的兴起.经济研究,2006,8:14~21.
[134]赵进文,闵捷.央行货币政策操作效果非对称性实证研究.经济研究, 2005, 2:32 ~40.
[135]郑江淮,何旭强,王华.上市公司投资的融资约束:从股权结构角度的实证分析.金融研究,2001,11:26~34.
[136]中国社科院经济增长前沿课题组.高投资、宏观成本与经济增长的持续性.经济研究,2005,10:12~17.
[137]周英章,蒋振声.货币渠道、信用渠道与货币政策有效性.金融研究,2002,9:34~41.
[2] Angeloni, I., Anil, K., Benoit, M. and Daniele, T. Monetary transmission in the Euro Area: Where do we stand. ECB Working Paper, 2002, No. 114.
[3] Auerbach. Taxation, corporate financial policy and the cost of capital. Journal of Economic Literature, 1983, 21: 905~940.
[4] Bernanke, Ben S., Blinder, Alan S. Credit, money and aggregate demand. The American Economic Review, 1988, 78:435~39.
[5] Bernanke, Ben S., Gertler.Inside the black box: the credit channel of monetary policy transmission. Journal of Economic Perspectives, 1995, 12: 27~48.
[6] Bernanke, Gertler, and Gilchrist.The financial accelerator and the flight to quality. The Review of Economic Perspectives, 1996, 9:27~48.
[7] Bernanke, Blinder. The federal funds rate and the channels of monetary transmission. American Economic Review, 1992, 9: 901 ~921.
[8] Bond, Cummins. Noisy share prices and the Q model of investment. The Institute for Fiscal Studies, Working paper, 2001.
[9] Bond, Klemm, and Smith. The roles of expected profitability, Tobin Q and cash flow in econometric models of company investment. The Institute for Fiscal Studies, working paper, 2004.
[10] Brissimis, Kamberoglou, and Simigiannis.Is there a bank lending channel of monetary policy in Greece? Evidence from bank level data.ECB Working Paper, 2001, No. 104.
[11] Brüggemann, Lütkepohl. Lag selection in subset VAR models with an application to a U.S. monetary system. Econometric Studies, 2001, 107~128.
[12] Butzen, Fuss and Vermeulen.The interest rate and credit channel in Belgium: An investigation with micro-level firm data. ECB Working Paper, No.107, 2001.
[13] Carcia, C.J., Restrepo, J.E. Price inflation and exchange rate pass-through in Chile. Working Paper of Central Bank of Chile, No. 128, 2000.
[14] Carpenter, Guariglia, A. Cash flow, investment, and investment opportunities: New tests using UK panel data. Economic School of University of Nottingham, Discussion Paper, 2003.
[15] Carrasquilla.Monetary policy transmission: The Colombian case.BIS Policy Papers, No. 3, 1998, 81~104.
[16] Cleary .International Corporate Investment and the Relationships Between Financial Constraint Measures.Journal of Banking & Finance ,2006,30:1559–1580.
[17] Chan, Tong.On estimating thresholds in autoregressive models. Journal of Time Series Analysis, 1986, 7:179~194.
[18] Chan. Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model. Annals of Statistics, 1993, 21:521~533.
[19] Chatelain, Tiomo.Investment, the cost of capital and monetary policy in the nineties in France: A panel data investigation. ECB Working Paper, No. 106, 2001.
[20] Chatelain, Generale, Kalkreuth, and Vermeulen.Firm investment and monetary transmission in the Euro Area countries. Frankfurt: Deutsche Bundes Bank No.20, 2001.
[21] Chirinko.Business fixed investment spending: modeling strategies, empirical results, and policy implications. Journal of Economic Literature, 1993, 16: 1875 ~1911.
[22] Chirinko. Investment , Tobin’s Q and multiple capital inputs . NBER Working Paper, 1986.
[23] Cochrane. What does the VARs Mean? Measuring the output effects of monetary policy. Journal of Monetary Economics, 1998, 41: 277~ 300.
[24] Cont.Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 2001, 1: 223~236.
[25] Cummins, Jason, Kevin, Hassett and Steven .Investment behavior, observable expectations, and internal funds. Mimeograph, New York University, 1999.
[26] Cunningham, A. and Haldane.The monetary transmission mechanism in the United Kingdom: Pass-through and policy rules. Working Paper of Central Bank of Chile, No. 83, 2002.
[27] De la Roca, J.The transmission mechanism of monetary policy in Peru.BIS Policy Papers, No. 8, 1998, 181~194.
[28] Deck G,Scheuer T.Threshold accepting:A general purpose optimization algorithm appearing uperior to simulated annealing.Journal of Computational Physics,1990,90(1):161~175.
[29] Dewatripont, Mathias and Jean Tirole.A theory of debt and equity: Diversity of securities and manager-shareholder congruence. Quarterly Journal of Economics, 1994, 109:1027~1054.
[30] Dijk, Ter?svirta, and Franses.Smooth transition autoregressive models-A survey of recent developments. Econometric Reviews, 2002, 21: 1~47.
[31] Efron.Bootstrap methods: another look at the Jakknife.Annals of Statistics, 1979, 7:1~26.
[32] Eisner, R., Nadiri, M. Investment behavior and the Neo-Classical theory. The Review of Economics and Statistics, 1968, 50:369~382.
[33] Enders, Granger. Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business and Economics Statistics, 1998, 16:304~311.
[34] Engle. Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflations. Econometrica, 1982, 50: 987~1007.
[35] Engle. New frontiers for ARCH Models. Journal of Applied Econometrics, 2002,17:425~446.
[36] Fama.Efficient capital markets: A review of theory and empirical work. Journal of Finance, 1970, 2:383~417.
[37] Fama.The behavior of stock-market prices. The Journal of Business, 1965, 38:34~106.
[38] Farinha, Marques. The bank-lending channel of monetary policy: Identification and estimation using Portuguese micro bank data.ECB Working Paper, No. 102, 2001.
[39] Fazzari, Hubbard and Petersen. Financing constraints and corporate investment. Brooking Papers on Economic Activity, 1988, 1:141~195.
[40] Fazzari, Athey. Asymmetric information, financing constraints, and investments. Review of Economics and Statistics ,1987,69:481~487.
[41] Franses, Van and Dijk.Non-linear time series models in empirical finance. Cambridge University Press .Cambridge, 2000.
[42] Gaiotti, Generale. Does monetary policy have asymmetric effects? A look at the investment decisions of Italian firms.ECB Working Paper, No. 110, 2001.
[43] Gertler, Gilchrist.Monetary policy, business cycles, and the behavior of small manufacturing firms. Quarterly Journal of Economics, 1994, 30:309-340.
[44] Gianni. Financial constraints, the user cost of capital and corporate investment in Australia. Reserve Bank of Australia, Working Paper, 2004.
[45] Gilchrist, Himmelberg and Huberman.Do stock bubbles influence corporate investment. NBER Working Paper, 2004.
[46] Gilchrist, Himmelberg. Evidence on the role of cash flow for investment. Journal of Monetary Economics, 1995, 36:541~572.
[47] Goffe, Ferrier, and Rogers. Global optimization of statistical functions with simulated annealing. Journal of Econometrics, 1994, 60: 65~99.
[48] Gonzalez, Ter?svirta, and Dijk.Panel smooth transition regression models.University of Technology Sydney, Working Paper, 2005.
[49] Grabowski, Mueller. Managerial and stockholder welfare models of firm expenditures. The Review of Economics and Statistics, 1972, 54: 9 ~ 24.
[50] Granger. Overview of nonlinear macroeconometric models. Macroeconomic Dynamics, 2001, 5: 466~481.
[51] Granger,Ter?svirta.Modelling nonlinear economic relationships. Oxford University Press. Oxford, 1993.
[52] Grassberger, Procaccia.Characterization of strange attractors. Physical Review Letters, 1983, 50:346~349.
[53] Gugler, K. , Mueller , D. C. and Yurtoglu , B. Marginal q , average q , cash flow and investment .Southern Economic Journal , 2003 , 70: 512~531.
[54] Hamilton. A parametric approach to flexible nonlinear inference .Econometrica, 2001, 69:537~573.
[55] Hamilton. A new approach to the economic analysis of nonstationary time series and the business cycle.Econometrica, 1989, 57:357~384.
[56] Hamilton. Time series analysis. Princeton University Press .Princeton, 1994.
[57] Hansen. Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics, 1999, 93:345~368.
[58] Hansen. Inference when a nuisance parameter is not identified under the null hypothesis.Econometrica, 1996, 64:413~430.
[59] Hay, D. A., Morris, D. J. Industrial economics and organization. Oxford University Press. Oxford, 1991.
[60] Hayashi, F. Tobin’s marginal and average q: A neoclassical interpretation. Econometrica. 1982, 50:213~224.
[61] Hayashi.The cost of capital, Q, and the theory of investment in econometrics and the cost of capital.The MIT Press. Cambridge, 2000.
[62] Hernando, Martinez. Is there a bank-lending channel of monetary policy in Spain?ECB Working Paper, No. 99, 2001.
[63] Hsiao. Autoregressive modeling and money-income causality detection. Journal of Monetary Economics, 1981, 7:85~106.
[64] Hsieh, D. Chaos and nonlinear dynamics: Application to financial markets. Journal of Finance, 1991, 5: 1839~1877.
[65] Huh.Estimating asymmetric output cost of lowering inflation for Australia. Southern Economic Journal, 2002, 68:600~616.
[66] Johansen.Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 1991, 59:1551~1580.
[67] Jorgenson, D. Capital theory and investment behavior .American Economic Review, 1963, 53: 247~259.
[68] Kaplan, Zirgales.Do financing constraints explain why investment is correlated with cash flow.NBER Working Paper, 1995.
[69] Kasuya. Regime-switching approach to monetary policy effects: Empirical studies using a smooth transition vector autoregressive model. Bank of Japan, Working Paper, 2003.
[70] Kazuo Ogawa.Monetary transmission and inventory: Evidence from Japanese balance-sheet data by firm size. The Japanese Economic Review, 2002, 53:425 ~443.
[71] Kim, Osborn and Sensier. Nonlinearity in the Fed`s monetary policy rule. CGBCR Discussion Paper ,018, 2002.
[72] Kirkpatrieks,Gelatt,and Vecch.Optimization by simulated annealing Science, 1983, 220:671~680.
[73] Koop, Pesaran and Potter. Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 1996, 74:119~147.
[74] Krolzig H.M. Markov-switching vector autoregressions: modeling, statistical inference and application to business cycle analysis. Journal of Econometrics,1997, 65:102~135.
[75] Laarhoven P , Aans E.Simulated annealing:Theory and applications.D.Reidel Publishing Company.Dordrecht, 1987.
[76] Lundbergh, Ter?svirta and Van Dijk.Time-varying smooth transition autoregressive models. Stockholm School of Economics, Working Paper Series in Economics and Finance NO.376, 2000.
[77] Loayza, Schmidt-Hebbel, K. Monetary policy functions and transmission mechanisms: An overview.Central Bank of Chile, 2002.
[78] Luukkonen, Saikkonen and Ter?svirtatesting .Linearity against smooth transition autoregressive models.Biometrika, 1988, 75, 3:491~499.
[79] Lyziak, T. Monetary transmission mechanism in Poland: Theoretical concepts vs. evidence. The National Bank of Poland, Working, Paper No. 19, 2001.
[80] Martinez, Sanchez, O. and Werner, A. Monetary policy and the transmission mechanism in Mexico. Papers Presented at Banco de México's 75th Anniversary Seminar Mexico City November 14-15, 2000, Banco de México, 197~261.
[81] McKinnon. Money and capital in economic development. The Brooking Institution, Washington DC, 1973.
[82] Michael, Henderson. STAR modeling for stocks and currencies. Global Investor, 1995 8:42~45.
[83] Misaico, Z.Q. Transmission mechanisms of monetary policy in an economy with partial dollarisation: the case of Peru.BIS Papers, No. 8, 2001.
[84] Mishkin, F. S. The economics of money, banking and financial markets. Wesley. New York,2003,123~127.
[85] Metropolis N.Equations of state calculations by fast computing machines.Journal Chem Phys,1953,21:1 087~1 091.
[86] Nychka, Ellner, Gallant and McCaffrey. Finding chaos in noisy systems. Journal of the Royal Statistical Society, 1992, 54:399~426.
[87] Omar. Investigating the differential impact of real interest rates and credit availability on private investment: Evidence from Venezuela. Dissertation Texas A&M University, 2001.
[88] Paul, Simpson, Denise, Osborn and Marianne Sensier.Modeling business cycle movements in the UK economy.Economica, 2001, 68:243~267.
[89] Pesaran, M., Potter, S. A floor and ceiling model of US output. Journal of Economic Dynamics and Control, 1997, 21:661~695.
[90] Pesaran M., Shin Y. Generalized impulse response analysis in linear multivariate models. Economic Letters, 1998, 58: 17~29.
[91] Sarantis.Nonlinearities, cyclical behavior and predictability in stock markets: International evidence. International Journal of Forecasting, 2001 17:459~482.
[92] Sensier, Osborn and Ocal. Asymmetric interest rate effects for the UK real economy. Oxford Bulletin of Economics and Statistics, 2002, 64:315~339.
[93] Shaw.Fiancial deepening in economic development. Oxford University Press. New York, 1973.
[94] Sims, Christopher. Comparison of interwar and postwar business cycles: Monetarism reconsidered. American Economic Review, 1980, 70.
[95] Sims. Interpreting the macroeconomic time series facts: the effects of monetary policy. European Economic Review, 1992, 36:975~1011.
[96] Stulz.Managerial discretion and optimal financing choices. Journal of Financial Economics, 1990, 26:3~28.
[97] Summers. Does the stock market rationally reflect fundamental values. Journal of Finance 1986, 41: 591 ~600.
[98] Swanson. Money and output viewed through a rolling window. Journal of Monetary Economics, 1998, 41: 455~ 473.
[99] Szu H,Hartley P.Fast simulated annealing.Physics Letter A,1987,122:157~162.
[100] Takashi Nagahata, Toshitaka Sekine.Firm investment, monetary transmission andbalance-sheet problems in Japan: an investigation using micro data. Japan and World Economy, 2005, 17:345~369.
[101] Ter?svirta, Anderson. Characterizing nonlinearities in business cycles using smooth transition autoregressive models. Journal of Applied Econometrics, 1992, 7:119~136.
[102] Ter?svirta.Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 1994, 89: 208~218.
[103] Tobin, J. A general equilibrium approach to monetary theory .Journal of Money, Credit and Banking. 1969, 1:15~29.
[104] Tong, Howell. On a threshold model. Pattern Recognition and Signal Processing, 1978, 100-141.
[105] Tong, Howell .Non-linear time series: a dynamical system approach. Oxford University Press. Oxford, 1990.
[106] Tsay. Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 1998, 93:1188~1202.
[107] Tyssedal and Tj?stheim.An autoregressive model with suddenly changing parameters. Applied Statistics, 1988, 37:353~369.
[108] Valderrama, M.Credit channel and investment behavior in Austria: A micro -econometric approach.ECB Working Paper, No. 108, 2001.
[109] Von Kalckreuth, U.Monetary transmission in Germany: New perspectives on finance constraints and investment spending.ECB Working Paper, No. 109, 2001.
[110] Weise.The asymmetric effects of monetary policy: A nonlinear vector autoregression approach. Journal of Money, Credit, and Banking, 1999.
[111] W.Enders,S.Hurn.Asymmetric price adjustment and the Phillips Curve. Journal of Macroeconomics,2002, 24:395~412
[112] Worms, A. The reaction of bank lending to monetary policy measures inGermany.ECB Working Paper, No. 96, 2001.
[113]北京大学中国经济研究中心宏观组.产权约束、投资低效与通货紧缩.经济研究,2004,9:10~18.
[114]程建胜.“九五”时期中国货币政策评述.海南金融,2002,2:23~28.
[115]陈飞,赵昕东,高铁梅.我国货币政策工具变量效应的实证分析.金融研究, 2002, 10:25~30.
[116]高铁梅.计量经济分析方法与建模:Eviews应用及实例.北京:清华大学出版社,2005.128~131.
[117]龚江辉.企业投资决定理论的新发展.华东经济管理,2006,6:89~91.
[118]郭明星,刘金全,刘志刚.我国货币政策供给增长率与国内产出增长率之间的影响关系检验——来自MS-VECM模型的新证据.数量经济技术经济研究, 2005, 5:16~23.
[119]蒋瑛琨,刘艳武,赵振全.货币渠道与信贷渠道传导机制有效性的实证分析:兼论货币政策中介目标的选择.金融研究, 2005,5:70~79.
[120]刘斌.货币政策冲击的识别及我国货币政策有效性的实证研究.金融研究,2001,7:36~43.
[121]刘明.信贷配给与货币政策效果非对称性及“阈值效应”分析.金融研究, 2006, 2:18~26.
[122]彭方平,王少平.我国利率政策的微观效应.管理世界,2007,1:45～52.
[123]孙明华.我国货币政策传导机制的实证分析.财经研究,2004,3:42~46.
[124]孙华妤,马跃.中国货币市场与股票市场的关系.经济研究, 2003,7:17~23.
[125]唐齐鸣,王海生.中国货币政策的回顾与展望.华中理工大学学报,1997,1:23~27.
[126]童盼,陆正飞.负债融资、负债来源与企业投资行为.经济研究,2005,5:42~49.
[127]汪辉.上市公司债务融资、公司治理与市场价值.经济研究,2003,8:35~42.
[128]王少平,李子奈.我国货币需求的协整分析及其政策建议.经济研究,2004,7: 13~19.
[129]王少平,彭方平.我国通货膨胀与通货紧缩的非线性转换.经济研究,2006,8:35～42.
[130]王宇.伯南克时代:格林斯潘之后的美联储货币政策展望.大连:东北财经大学,2006.
[131]王新洲.非线性模型参数估计理论与应用.武汉:武汉大学出版社,2002.
[132]伍旭川,谢云山.“十五”货币政策回顾及展望.银行家,2006,3:17~21.
[133]徐亚平.货币政策有效性与货币政策透明制度的兴起.经济研究,2006,8:14~21.
[134]赵进文,闵捷.央行货币政策操作效果非对称性实证研究.经济研究, 2005, 2:32 ~40.
[135]郑江淮,何旭强,王华.上市公司投资的融资约束:从股权结构角度的实证分析.金融研究,2001,11:26~34.
[136]中国社科院经济增长前沿课题组.高投资、宏观成本与经济增长的持续性.经济研究,2005,10:12~17.
[137]周英章,蒋振声.货币渠道、信用渠道与货币政策有效性.金融研究,2002,9:34~41.