沪市A股交易量与收益率及波动性的关系--基于混合分布模型的实证研究

本文以混合分布模型为理论框架,运用Granger因果检验和扩展GARCH模型实证研究了上海A股市场交易量与股价之间的关系.得到了如下结论交易量变量和收益率、波动率存在双向Granger因果关系;交易量只能在有限程度内解释波动率持续性;上海股市的信息传播方式基本符合混合分布假设.     

稀疏组Lasso Granger因果图模型及应用

辨识序列间的因果联系是时间序列分析的主要任务之一。将Granger因果关系的检验问题转换为变量选择问题,应用稀疏组Lasso方法辨识序列间Granger因果关系的存在性和因果影响的滞后阶数。提出了稀疏组Lasso Granger因果图模型,顶点表示多维时间序列的分量序列,顶点间的有向边表示序列间存在的Granger因果关系,定义了滞后信息矩阵揭示因果影响的滞后信息。数值模拟验证了在各种维数和滞后影响结构的模型下,样本量对估计效果的影响。应用到中国宏观经济数据,进行实证分析的结果表明,稀疏组Lasso Granger因果图方法能够较好地揭示序列间的因果关系结构。     

ACSI模型的因果关系检验研究

根据美国顾客满意度指数(ACSI)模型中的各组成要素之间的因果联系,提出一种应用Granger因果关系检验方法来检验ACSI模型中各组成要素之间因果关系的方法.     

基于Granger因果图方法的物价水平国际间传递分析

目前,对Granger因果关系的研究大多数采用两变量Granger因果检验法,由于忽视其它重要变量的影响,常会导致虚假因果关系的出现。鉴此,采用Granger因果图模型方法分析中国及其主要贸易伙伴国(地区)间的物价传递,研究结果表明:美国在物价传递中发挥着主导作用,物价国际间传递存在一定的区域效应;除和中国香港地区存在即期因果关系外,中国对主要贸易伙伴国(地区)的物价水平基本无显著影响,中国既无输出通货膨胀也无输出通货紧缩。同时,样本期内中国物价水平呈现明显的外部"输入性"特征。因此,中国政府应采取措施应对国际的物价冲击,同时防范物价输入性引发的风险,以实现中国物价的稳定。     

环渤海地区产业结构转型与经济增长研究

文章结合产业结构转型系数,运用Granger因果检验方法和PDL(Polynomial Distribution lag)模型检验了环渤海地区产业结构转型对经济增长率和增长质量的影响.结果显示:不存在产业结构转型与经济增长率的长期因果关系,存在产业转型与TFP经济增长贡献率的单向因果关系.经济增长滞后于产业转型,即期产业转型系数与经济增长率负相关,但与滞后三期以内的产业转型系数正相关.因此,环渤海地区产业转型具有短期经济增长效应,不具备长期经济增长效应,环渤海地区产业提高了该区域的经济增长质量,同时也表明环渤海地区产业转型滞后,转型质量不佳.     

中国制造业PMI与CPI的相关性分析

文章选取了2005年1月至2015年4月的中国制造业采购经理指数(PMI)与居民消费价格指数(CPI)的数据进行相关性分析,并对两个时间序列构建向量自回归模型,进而通过Granger因果检验、脉冲响应函数分析、方差分解分析说明了中国制造业PMI与CPI之间的定性定量关系,得出PMI与CPI互为Granger因果关系,PMI与CPI之间存在相互作用关系,且PMI对CPI具有较长时间和较大程度的正向影响.     

陕西上市公司GDP贡献及可持续发展研究

选取陕西省在上海和深圳证券交易所的上市公司为样本.利用Eviews5.0对1997-2006年陕西GDP和所选上市公司净资产收益率、每股收益以及主营业务增长率进行了ADF单位根检验、Johansen协整检验和格兰杰(Granger)因果检验,发现陕西省经济发展数据和上市公司指标都是一阶平稳序列,且存在两个协整关系,上市公司的每股收益与GDP还具有反常的Granger因果关系.在陕西经济持续稳定增长的同时,上市公司的每股收益却逐年下降,可持续发展能力匮乏.     

我国制造业PMI与宏观经济景气指数关系的实证分析

文章采用2005年1月至2014年3月的中国制造业采购经理指数(PMI)数据以及宏观经济景气指数数据进行描述性相关分析,选取制造业PMI与宏观经济景气一致指数构建向量自回归VAR模型,进行Granger因果检验说明两指数之间存在双向因果关系,进一步利用脉冲响应函数和方差分解的分析方法,研究两指数的相互作用关系,得到PMI对宏观经济景气一致指数具有较长时间和较大程度的正向影响,从定性和定量两方面说明制造业PMI对国家宏观经济的先行影响作用.     

Granger因果关系检验的模型选择与相关策略研究

运用Granger因果关系检验识别确定经济变量间因果关系是经济研究中极为常见的分析模式,然而在具体应用时,Granger因果关系检验的功效会受到模型形式选择与检验策略因素的影响,为此,解析了Granger因果关系检验的水平型VAR、差分型VAR、VEC三种模型形式选择的基本原理,探讨了与模型选择相关的四大检验策略,即变量个数选择、滞后阶数选择、变量单整性检验、协整空间维数选择,并给出了Granger因果关系检验相对稳妥的实践操作程序。     

分位数回归下的因果关系检验

Granger因果关系在计量经济学中占有重要地位。在实践中,采用Granger检验是在最小二乘回归下进行的,检验的是条件均值之间的因果关系,在此基础上扩充到检验条件分位数上的因果关系,即线性分位数回归下的Sup-Wald检验法;利用该检验法实证分析全国城镇居民人均可支配收入增量对人均消费性支出增量之间的关系,结果表明其之间存在单向的因果关系,并且在较低分位数时城镇居民人均可支配收入增量对人均消费支出增量的因果关系并不显著,只有在分位数较高时这种因果关系才显著存在。     

A test of separate hypotheses for comparing linear mixed models with non nested fixed effects

As researchers increasingly rely on linear mixed models to characterize longitudinal data, there is a need for improved techniques for selecting among this class of models which requires specification of both fixed and random effects via a mean model and variance-covariance structure. The process is further complicated when fixed and/or random effects are non nested between models. This paper explores the development of a hypothesis test to compare non nested linear mixed models based on extensions of the work begun by Sir David Cox. We assess the robustness of this approach for comparing models containing correlated measures of body fat for predicting longitudinal cardiometabolic risk.     

中国城乡居民消费差距与收入差距的误差修正模型研究

在以绝对收入假说为基础的消费函数分析中发现,消费倾向差距对城乡居民消费差距的影响不显著,收入差距对城乡居民消费差距影响显著。在此基础上,通过构造城乡居民消费差距与收入差距的误差修正模型,发现他们之间还存在着长期稳定的均衡关系。格兰杰因果检验表明:在10%的显著性水平下,城乡居民消费差距与收入差距有双向的因果关系。为此,要有效缩小城乡居民消费差距,需要消费政策与收入政策协同并进。     

The effect of spillover on the Granger causality test

In this paper, we investigate the properties of the Granger causality test in stationary and stable vector autoregressive models under the presence of spillover effects, that is, causality in variance. The Wald test and the WW test (the Wald test with White's proposed heteroskedasticity-consistent covariance matrix estimator imposed) are analyzed. The investigation is undertaken by using Monte Carlo simulation in which two different sample sizes and six different kinds of data-generating processes are used. The results show that the Wald test over-rejects the null hypothesis both with and without the spillover effect, and that the over-rejection in the latter case is more severe in larger samples. The size properties of the WW test are satisfactory when there is spillover between the variables. Only when there is feedback in the variance is the size of the WW test slightly affected. The Wald test is shown to have higher power than the WW test when the errors follow a GARCH(1,1) process without a spillover effect. When there is a spillover, the power of both tests deteriorates, which implies that the spillover has a negative effect on the causality tests.     

Book Reviews

The diagnostic tools examined in this article are applicable to regressions estimated with panel data or cross-sectional data drawn from a population with grouped structure. The diagnostic tools considered include (a) tests for the existence of group effects under both fixed and random effects models, (b) checks for outlying groups, and (c) specification tests for comparing the fixed and random effects models. A group-specific counterpart to the studentized residual is introduced. The methods are illustrated using a hedonic housing price regression.     

A revisitation of the export-led growth hypothesis in Malaysia using the leveraged bootstrap simulation and rolling causality techniques

According to the neoclassical growth theory, export expansion could stimulate economic growth because it promotes specialisation and raises factor productivity. Thus, many developing countries depend heavily on export-orientated businesses to accelerate economic growth. Nevertheless, the causality evidences on the export-led growth hypothesis remain elusive and controversial. Two primary empirical questions emerged in the international trade and development literatures are: (a) Does the export-led growth hypothesis still valid? (b) Why causality evidences are inconsistent among studies? In light of these, the present study attempts to contribute to the export-led growth literature by using the Malaysian data set. This study covers the monthly data set from January 1975 to August 2010. To achieve the objectives of this study, we employ the leveraged bootstrap simulation causality test and also the rolling regression-based causality tests. The leveraged bootstrap simulation causality results suggest that exports and output growth are bilateral causality in nature. However, the rolling causality results demonstrate that the causality inferences for export-led growth hypothesis are unstable over time. For this reason, policy initiative to promote exports may not always stimulate economic growth and development in Malaysia. Therefore, balancing policy is urged to ensure that the economic growth in Malaysia can be materialised.     

Vector autoregression and causality: a theoretical overview and simulation study

This paper provides a theoretical overview of Wald tests for Granger causality in levels vector autoregressions (VAR's) and Johansen-type error correction models (ECM's). The theory is based on results in Toda and Phillips (1991a) and allows for stochastic and deterministic trends as well as arbitrary degrees of cointegration. We recommend some operational procedures for conducting Granger causality tests that are based on the Gaussian maximum likelihood estimation of ECM's. These procedures are applicable in the important practical case of testing the causal effects of one variable on another group of variables and vice versa. This paper also investigates the sampling properties of these testing procedures through simulation exercises. Three sequential causality tests in ECM's are compared with conventional causality tests in levels and differences VAR's.     

Detecting homogenous predictors in high-dimensional panel model with an MCMC algorithm

In panel data analysis, predictors may impact response in substantially different manner. Some predictors are in homogenous effects across all individuals, while the others are in heterogenous way. How to effectively differentiate these two kinds of predictors is crucial, particularly in high-dimensional panel data, since the number of parameters should be greatly reduced and hence lead to better interpretability by homogenous assumption. In this article, based on a hierarchical Bayesian panel regression model, we propose a novel yet effective Markov chain Monte Carlo (MCMC) algorithm together with a simple maximum ratio criterion to detect the predictors in homogenous effects in high-dimensional panel data. Extensive Monte Carlo simulations show that this MCMC algorithm performs well. The usefulness of the proposed method is further demonstrated by a real example from China financial market.     

A Method for Simulating Multivariate Non Normal Distributions with Specified Standardized Cumulants and Intraclass Correlation Coefficients

Intraclass correlation coefficients (ICCs) are commonly used indices in subject areas such as biometrics, longitudinal data analysis, measurement theory, quality control, and survey research. The properties of the ICCs most often used are derived under the assumption of normality. However, real-world data often violate the normality assumption. In view of this, a computationally efficient procedure is developed for simulating multivariate non normal continuous distributions with specified (a) standardized cumulants, (b) Pearson intercorrelations, and (c) ICCs. The linear model specified is a two-factor design with either fixed or random effects. A numerical example is worked and the results of a Monte Carlo simulation are provided to demonstrate and confirm the methodology.     

Misspecification in random coefficient regression models: A Monte Carlo simulation

A particularly useful approach for analyzing pooled cross-sectional and time series data is Swamy's random coefficient regression (RCR) model. This paper examines the performance of estimators and tests associated with this model under the assumptions that a) all coefficients are random and b) some or all of the coefficients are fixed. The latter case is a misspecification of the pure random coefficient model. Performance of the RCR model is examined in both cases and recommendations are made from the results of the analyses. Comparisons are made with a classical pooling technique.     

Heterogenous market hypothesis evaluation using multipower variation volatility

High-frequency trading activities are one of the common phenomena in nowadays financial markets. Enormous amounts of high-frequency trading data are generated by huge numbers of market participants in every trading day. The availability of this information allows researchers to further examine the statistical properties of informationally efficient market hypothesis (EMH). Heterogenous market hypothesis (HMH) is one of the important extensions of EMH literature. HMH introduced nonlinear trading behaviors of heterogenous market participants instead of normality assumption under the EMH homogenous market participants. In this study, we attempt to explore more high-frequency volatility estimators in the HMH examination. These include the bipower, tripower, and quadpower variation integrated volatility estimates using Heterogenous AutoRegressive (HAR) models. The empirical findings show that these alternatives multipower variation (MPV) estimators provide better estimation and out-of-sample forecast evaluations as compared to the standard realized volatility. In other words, the usage of MPV estimators is able to better explain the HMH statistically. At last, a market risk determination is illustrated using value-at-risk approach.