货币信贷结构对宏观经济的机理性影响——兼对“中国货币迷失之谜”的再解释

本文使用机理变化型变结构协整模型实证分析了1996年1月至2013年5月期间货币信贷结构对中国宏观经济的影响。结果表明:货币信贷结构均在特定时点开始对产出和通货膨胀率产生机理性影响;货币结构与信贷期限结构对实际产出分别具有较弱的正向和负向机理性影响,对通货膨胀率均具有一定程度的正向机理性影响,并且货币信贷结构对通货膨胀率的影响程度明显大于货币信贷总量。在此基础上,通过比较分析货币信贷结构与“货币迷失”之间的关系,发现货币结构与信贷期限结构对“中国货币迷失之谜”均具有很强的解释力。政策建议如下:中国人民银行应同时注重货币信贷总量与结构的调控,监测货币信贷结构的变动应成为提高货币政策有效性与制定货币政策的重要参考。     

资本管制能否抑制资本外逃--关于中国的案例分析

关于资本管制抑制资本外逃的有效性问题,国内外学者曾采用多种计量模型进行过深入的分析,但没有得出统一的结论.运用经济计量分析中最新的协整分析方法--边界检验(Bounds Testing)和自回归分布滞后模型(Autoregressive Distributed Lag Model,ARDL)对中国从1980～2003年的资本外逃状况进行分析的结果表明,无论是从短期还是从长期来看,资本管制对我国的资本外逃都有显著的抑制作用.     

几乎理想需求系统面板数据模型的非参数估计分析

本文探讨几乎理想需求系统的估计问题,充分考虑到各类商品价格数据的局限性和模型中各类商品消费份额的联立内生性,逐步建立一个可以得到估计的几乎理想需求系统非参数面板数据模型;给出其非参数局部线性估计方法,并将非参数估计方法应用于珠三角地区城镇居民消费支出结构的估计和分析,由此对消费支出进行弹性分析。     

“港股直通车”对两地股市影响的实证分析

本文以股权分置改革后的最新数据为研究对象,首先剖析了香港股市与内地股市的高度关联性,然后运用计量经济方法检验两地股市的相关性、以及“港股直通车”前后两地股市关系发生的变化,从而得出结论：在当前背景下,不宜推出“港股直通车”。这也证明了中央暂停港股直通车的合理性与及时性。     

A new method for measuring congestion in data envelopment analysis

In data envelopment analysis (DEA), there are two principal methods for identifying and measuring congestion: Those of Färe et al. [Färe R, Grosskopf S. When can slacks be used to identify congestion. An answer to W. W. Cooper, L. Seiford, J. Zhu. Socio-Economic Planning Sciences 2001;35:1–10] and Cooper et al. [Cooper WW, Deng H, Huang ZM, Li SX. A one-model approach to congestion in data envelopment analysis. Socio-Economic Planning Sciences 2002;36:231–8]. In the present paper, we focus on the latter work in proposing a new method that requires considerably less computation. Then, by proving a selected theorem, we show that our proposed methodology is indeed equivalent to that of Cooper et al.     

FDI、对外贸易与经济增长的协整性研究——以山东省为例

关于利用外资、进出口贸易与经济增长关系的研究一直以来备受关注,但近年来的研究大多致力于两者间关系的独立性研究,其结果往往会使得理论与实证研究的解释力受到影响。对于多个同阶序列变量之间是否存在长期稳定的均衡关系的研究,协整理论具有重要的应用,其既可充分利用信息资源,还有效避免了“伪回归”问题。另外鉴于不同区域问经济发展模式、发展水平的差异性,各经济要素之间的关系也不尽相同,本文采用山东省1984～2007年间24年的统计数据,对该省的外商直接投资、进出口贸易与经济增长之间的相互关系进行了协整性检验和误差修正模型分析,总结得到有关结论：山东省利用外资、进出口贸易与其经济增长之间存在长期均衡关系,但具有区别于其他地区的自身特点。最后,针对山东经济的协调发展提出了若干政策建议。     

FDI的资本效应与溢出效应之比较——基于中国数据的协整分析

本文利用一个修正的经济增长分析框架,同时容纳FDI对经济增长的直接资本效应和间接溢出效应,然后在协整检验的基础上,使用向量误差修正模型、冲击响应函数和Granger因果关系检验等方法,对中国1983-2007年间的数据进行实证研究,考察了GDP和其他多个经济变量之间的长期和短期均衡关系,并且对FDI的两种效应做出了比较分析。研究发现,FDI的资本效应比较明显,而溢出效应比较微弱。     

山东省出口增长与要素禀赋的协整分析

根据Granger因果检验方法和协整理论,利用山东省1986～2007年的经济数据,对山东省要素禀赋对出口增长的影响进行了实证分析。结果表明,山东省出口增长与要素禀赋之间存在单向的Granger因果关系,出口增长与要素禀赋之间存在着长期稳定的均衡关系。要素禀赋对出口增长有显著的促进作用,因此增加人均固定资本有利于外贸出口的增长[1]。     

Nonlinear Mean Reversion across National Stock Markets: Evidence from Emerging Asian Markets

This paper seeks empirical evidence of nonlinear mean-reversion in relative national stock price indices for Emerging Asian countries. It is well known that conventional linear unit root tests suffer from low power against the stationary nonlinear alternative. Implementing the nonlinear unit root tests proposed by Kapetanios et al. (2003 Kapetanios, G., Shin, Y. and Snell, A. 2003. Testing for a unit root in the nonlinear STAR framework. Journal of Econometrics, 112: 359–379. [Crossref], [Web of Science ®] , [Google Scholar]) and Cerrato et al. (2009 Cerrato, M., de Peretti, C., Larsson, R. and Sarantis, N. 2009. “A nonlinear panel unit root test under cross section dependence”. Working Papers 28, Department of Economics, University of Glasgow [Google Scholar]) for the relative stock prices of Emerging Asian markets, we find strong evidence of nonlinear mean reversion, whereas linear tests fail to reject the unit root null for most cases. We also report some evidence that stock markets in China and Taiwan are highly localized.     

Basket trading under co-integration with the logistic mixture autoregressive model

In this paper, we propose a co-integration model with a logistic mixture auto-regressive equilibrium error (co-integrated LMAR), in which the equilibrium relationship among cumulative returns of different financial assets is modelled by a logistic mixture autoregressive time series model. The traditional autoregression (AR) based unit root test (ADF test), used in testing co-integration, cannot give a sound explanation when a time series passes the ADF test. However, its largest root in the AR polynomial is extremely close to, but less than, one, which is most likely the result of a mixture of random-walk and mean-reverting processes in the time series data. With this background, we put an LMAR model into the co-integration framework to identify baskets that have a large spread but are still well co-integrated. A sufficient condition for the stationarity of the LMAR model is given and proved using a Markovian approach. A two-step estimating procedure, combining least-squares estimation and the Expectation-Maximization (EM) algorithm, is given. The Bayesian information criterion (BIC) is used in model selection. The co-integrated LMAR model is applied to basket trading, which is a widely used tool for arbitrage. We use simulation to assess the model in basket trading strategies with the statistical arbitrage feature in equity markets. Data from several sectors of the Hong Kong Hang Seng Index are used in a simulation study on basket trading. Empirical results show that a portfolio using the co-integrated LMAR model has a higher return than portfolios selected by traditional methods. Although the volatility in the return increases, the Sharpe ratio also increases in most cases. This risk–return profile can be explained by the shorter converging period in the co-integrated LMAR model and the larger volatility in the ‘mean-reverting’ regime.