基于非参数回归模型的货币供应与通货膨胀的实证分析

通货膨胀是当前我国国民经济发展中遇到的热点问题,本文从探讨与通货膨胀率关系最直接的变量货币供应量出发,首先定性的讨论它们之间的有关理论和关系,然后通过实际数据,采用R软件,用LPE回归模型对货币供应与通货膨胀的因果关系进行实证分析,并将此结果与一般参数回归模型比较,得出LPE回归模型明显优良的结论,最后,通过实证结果分析,得出相应的结论。     

我国货币流通量与通货膨胀动态关系的非参数回归分析

为研究我国货币流通量与通货膨胀之间的动态关系,本文将非参数局部线性估计理论引入到回归模型中,建立通货膨胀非参数回归模型并和传统的线性回归模型进行比较,发现传统的线性回归模型不能理想的捕获我国货币流通量与通货膨胀之间的动态关系。     

我国货币流通量与通货膨胀动态关系的非参数回归分析

为研究我国货币流通量与通货膨胀之间的动态关系,本文将非参数局部线性估计理论引入到回归模型中,建立通货膨胀非参数回归模型并和传统的线性回归模型进行比较,发现传统的线性回归模型不能理想的捕获我国货币流通量与通货膨胀之间的动态关系.     

从通货膨胀到货币供给

货币既是推动经济增长的动力,又是诱发通货膨胀的原因。文章通过对我国1985年～1999年以来的通货膨胀回顾,从理论和实证两方面论述我国货币供应量与通货膨胀的关系。     

货币供给、通货膨胀、经济增长之间关系的实证研究——基于2006～2010年数据的分析

利用我国2006年12月～2010年12的月度数据,在VAR模型的基础上,对我国货币供应、通货膨胀、经济增长之间的关系进行实证分析。研究发现,短期内通货膨胀和经济增长对货币供应量的效应相反;货币供应并不影响通货膨胀和经济增长;经济增长与通货膨胀无关,通货膨胀对经济增长具有正面效应。长期来看,通货膨胀和货币供应量之间正相关;经济增长对货币供应具有负效应,货币供给量的增加促进经济增长,证实我国存在托宾效应;经济增长和通货膨胀之间作用相反,通货膨胀损害经济增长的弗里德曼假说在我国适用。同时得出我国货币供给具有非中性和内生性特点的结论。     

中国经济增长与通货膨胀间非对称关系的实证分析

利用1985—2012年中国主要宏观经济变量数据,采用门限自回归模型实证检验了通货膨胀对经济增长的非线性影响。研究结果表明,通货膨胀门限效应明显存在;在门限值以上,通货膨胀对经济增长起阻碍作用;在门限值以下,通货膨胀对经济增长起促进作用。在此基础上,利用平滑转换回归模型进一步检验了通货膨胀与经济增长之间的非线性关系,不同模型的实证结果都支持通货膨胀门限效应存在。     

中国经济增长与通货膨胀间非对称关系的实证分析

利用1985—2012年中国主要宏观经济变量数据,采用门限自回归模型实证检验了通货膨胀对经济增长的非线性影响。研究结果表明,通货膨胀门限效应明显存在;在门限值以上,通货膨胀对经济增长起阻碍作用;在门限值以下,通货膨胀对经济增长起促进作用。在此基础上,利用平滑转换回归模型进一步检验了通货膨胀与经济增长之间的非线性关系,不同模型的实证结果都支持通货膨胀门限效应存在。     

货币供应、通货膨胀与经济增长关系的实证研究——基于分位数回归方法

使用1985—2011年的数据,运用单位根检验、格兰杰因果关系检验以及建立分位数回归模型,对中国货币供应、通货膨胀与经济增长之间的关系进行了实证研究,结果表明：货币供应、经济增长均会导致通货膨胀,适度的通货膨胀会促进经济增长,二者呈双向因果关系。货币供应和经济增长对通货膨胀的影响都具有一定的时滞性。在控制其他变量不变的情况下,经济增长对通货膨胀的影响比货币供应要大。当通货膨胀水平较低时,经济增长和货币供应对通货膨胀的影响较小,当通货膨胀处于较高水平时,货币供给对通货膨胀的影响相对较大。     

货币政策对物价稳定和经济增长的效应——中国与巴基斯坦的比较研究

本文采用自回归分布滞后模型对中国和巴基斯坦两国货币政策、通货膨胀与经济增长之间的长期关系进行了比较研究,由此得出如下几点结论:第一,两国的经验数据都印证了经济学家们普遍接受的通货膨胀是一种货币现象的推论;第二,货币非中性假说在两国的经验数据中均无法被拒绝;第三,在中国存在货币数量论所描述的货币供给与通货膨胀同步增长的预测关系,而这种关系在巴基斯坦却不存在;第四,两国若想实现较高的经济增长都必须在长期内降低通货膨胀率。     

货币供应增长率与通货膨胀之间动态关系的实证研究

中国实施宽松的货币政策,货币供应量增幅快速回升,流动性加大,市场的通涨预期不断高涨。故系统研究货币供应量、银行信贷与通货膨胀之间的动态关系,可为衡量货币政策有效性提供些依据。通过构建向量自回归模型,分析货币供应、金融机构贷款和通货膨胀之间的动态关系。     

Semiparametric EGARCH model with the case study of China stock market

In this paper, we propose a new semiparametric method for GARCH model by combining the EGARCH (1,1) model and local polynomial regression. Based on the idea of two-stage estimate, a link function is estimated by the local polynomial and then the parameters are obtained via the weighted least square method. Finally we apply this method to the Shanghai Composite Index in the China stock market and compared the results with these of EGARCH.     

上市公司财务困境预测模型的再比较

该文将中国上市公司因财务状况异常而被特别处理(ST)作为企业陷入财务困境的标志,采用主成分分析方法确定模型变量,并利用多元判别分析、Logistic 回归和改进型 BP 神经网络三种方法进行财务困境预测.比较其预测结果发现,BP 神经网络模型的预测准确率明显优于多元判别分析和Logistic 回归模型,而后两者的判别效果接近,可见改进型 BP 神经网络模型更适合于企业财务困境预测.但三种模型的长期预警能力均不够理想,需要建立以定量模型为主、定性分析为辅的上市公司财务困境预测方式,以提高预测的准确性.     

Estimation of hedonic price functions via additive nonparametric regression

We model a hedonic price function for housing as an additive nonparametric regression. Estimation is done via a backfitting procedure in combination with a local polynomial estimator. It avoids the pitfalls of an unrestricted nonparametric estimator, such as slow convergence rates and the curse of dimensionality. Bandwidths are chosen using a novel plug in method that minimizes the asymptotic mean average squared error (AMASE) of the regression. We compare our results to alternative parametric models and find evidence of the superiority of our nonparametric model. From an empirical perspective our study is interesting in that the effects on housing prices of a series of environmental characteristics are modeled in the regression. We find these characteristics to be important in the determination of housing prices.First version received: October 2002/Final version received: October 2003We thank B. Baltagi and two anonymous referees for their comments. The authors retain responsibility for any remaining errors.     

中国省区经济效率及其影响因素分析——来自2000～2005年的经验证据

探寻产生中国各省区之间越来越明显的经济发展水平差距的根源,对于构建和谐社会、维持经济的健康持续发展具有重要意义.而经济发展水平与区域内的投入产出紧密相连,为此借助数据包络分析方法,构建理论分析模型,测算省区经济发展效率,进而构建回归模型,分析影响效率的因素.基于理论分析框架,利用中国2000-2005年的统计数据对31个省区的X效率进行测算,并对影响效率的因素进行分析.研究结果表明,经济发展水平不同省区的效率存在显著差异,即各省区之间经济发展水平上的差距源于其利用现有资源禀赋获取最大化产出能力的差异.而对影响效率因素的分析则表明,人口素质与区位优势对效率具有显著的正向作用,而金融深化、外商直接投资在总体上却没有表现出理论上所认为的正向效果.最后,根据实证研究的结果,提出了一系列政策建议.     

The relevance of commuting zones for regional spending efficiency

We use data envelopment analysis (DEA) efficiency scores to show that clustering municipalities into encompassing regional clusters improves spending efficiency of single stand-alone municipalities. We propose a new geographic aggregation based on municipalities-to-municipalities commuting flows, defined using hierarchical cluster analysis. Our example for Portugal shows that from an output-oriented perspective between 83% and 98% of municipalities would increase their efficiency scores, while from an input-oriented perspective between 86% and 98% of municipalities would also be better off in terms of efficiency. Then using a linear regression model, we find that population increases positively affects the efficient scores (via scale economies). Also, increases in the share of high-educated and poorer residents leads to higher efficiency scores.     

Second-order polynomial spatial error model. Global and local spatial dependence in unemployment in Andalusia

This paper analyses a second-order polynomial spatial structure in the residues of a regression model. We propose a new specification that captures spatial dependence on two different levels, adding a new autoregressive cycle to the errors of the classical spatial error model (SEM). The inference problems of the parameters are solved by means of maximum likelihood estimation. The model is confirmed to identify two spatial structures of spatial dependence, global and local, by an empirical application in the analysis of municipal unemployment in the Spanish region of Andalusia. Finally, Monte Carlo is implemented to evaluate the performance of this strategy in a context of finite size samples.     

Polynomial approximation of distributed lags and linear restrictions: A Bayesian approach

In this paper, the polynomial approximation of distributed lags is investigated within the framework of linear restrictions in linear regression models. In the first part, the polynomial approximation is analysed assuming well known the truncation point and the degree of the polynomial. The polynomial approximation is shown to involve linear restrictions on regression coefficients; two equivalent representations of these restrictions are used to clarify relationships between previous works byAlmon and byShiller. The difficulties related to the treatment of exact restrictions in a Bayesian framework are then tackled in the present context and alternative procedures are presented. In the second part, the analysis is extended to the case of unknown truncation point and/or unknown degree of the polynomial. This leads to consider mixed prior distributions as for the problem of choosing among different models. The paper ends by investigating the sensitivity of a particular set of data w.r.t. changes in the truncation point, in the degreee of the polynomial and in the prior tightness of the polynomial approximation.     

Model selection for nonlinear time series

We investigate the finite-sample performance of model selection criteria for local linear regression by simulation. Similarly to linear regression, the penalization term depends on the number of parameters of the model. In the context of nonparametric regression, we use a suitable quantity to account for the Equivalent Number of Parameters as previously suggested in the literature. We consider the following criteria: Rice T, FPE, AIC, Corrected AIC and GCV. To make results comparable with other data-driven selection criteria we consider also Leave-Out CV. We show that the properties of the penalization schemes are very different for some linear and nonlinear models. Finally, we set up a goodness-of-fit test for linearity based on bootstrap methods. The test has correct size and very high power against the alternatives investigated. Application of the methods proposed to macroeconomic and financial time series shows that there is evidence of nonlinearity.First version received: September 2002/Final version received : October 2003I would like to thank Cees Diks, Cars Hommes and an anonymous referee for useful comments that significantly improved the paper.     

中国技术进步源泉的比较：自主创新和技术外溢——基于阿尔蒙多项式分布滞后模型的研究

在内生增长理论中,技术溢出和自主创新都是技术进步的重要源泉。通过Granger因果检验发现,国内外研发和国内专利与中国劳动生产率和全要素生产率之间存在因果关系。利用阿尔蒙多项式分布滞后模型对国内外研发、国内专利与中国劳动生产率以及全要素生产率之间的关系进行比较研究发现,国外研发对中国劳动生产率和全要素生产率增长的促进作用远大于国内研发和专利,国际技术溢出是中国技术进步的主要源泉,劳动生产率比全要素生产率能够更好地度量中国技术进步。     

Intraday stock prices, volume, and duration: a nonparametric conditional density analysis

We investigate the distribution of high-frequency price changes, conditional on trading volume and duration between trades, on four stocks traded on the New York Stock Exchange. The conditional probabilities are estimated nonparametrically using local polynomial regression methods. We find substantial skewness in the distribution of price changes, with the direction of skewness dependent on the sign of trade. We also find that the probability of larger price changes increases with volume, but only for trades that occur with longer durations. The distribution of price changes vary with duration primarily when volume is high.