关联效应还是传染效应

用带有不可观测变量的动态层级模型,同时分析关联效应和传染效应;量化不同时间范围内,上海市场板块之间关联效应与传染效应的动态变化;考察危机爆发时,两种效应对市场联动性的影响,并对板块之间风险传染的路径和方向进行检验.结果表明:在市场平稳或者突发危机时,关联效应都是引起板块间联动性的主要原因.危机事件爆发时,板块之间联动性增加,但关联效应和传染效应并不存在严格意义上的此消彼长关系,在危机爆发的中心区域二者都出现了不同程度的上升,传染效应增速更快.板块之间的传染路径存在典型的非对称性,在56条可能的传染路径中,只有16条路径存在传染效应.     

中国股市动量效应与反转效应形成机制研究

针对中国股市的特殊性,文章在HS模型基础上将投资者划分为消息观察者、动量交易者和套利惯性投资者,从他们对股票基本面和技术面关注点的不同出发,分段研究了三类投资者不同决策对股价的影响.分析表明:股价波动以消息传播为始点,动量交易者与套利惯性投资者加剧了动量效应,套利惯性投资者还会引发股价反转效应；股价产生动量效应和反转效应的程度受到股票市场环境、投资者规模以及风险承受能力的影响.     

投资者情绪对规模效应的影响

规模效应是一种常见的金融市场异象,在新兴资本市场对资产价格影响非常显著,行为金融理论用投资者情绪可解释其规模效应。采用网络文本技术与主成分法相结合,构建投资者情绪指标,以此度量市场情绪,并研究市场情绪与规模效应之间的作用机理。实证结果表明:样本期内中国股市存在显著的规模效应,投资者情绪与规模效应成正向关系,即当期情绪越高涨,规模效应越显著;由于小盘股易受到投资者情绪的影响,拉动小盘股与大盘股收益率的差值,从而造成规模效应在不同情绪期间的差异。     

投资者情绪累积效应与股票价格波动

投资者情绪与股票价格波动是行为金融领域的前沿问题。文章选取A股上市公司2011—2020年交易数据,构造一个全新的日频情绪指标,并基于该指标研究短期内投资者情绪的累积变化对股票收益的影响。结果表明：投资者情绪具有累积效应,该效应最初带来显著的正收益,但会在投资者情绪累积到一定水平后,引致较大的价格修正,这对难以套利的股票影响更为明显;高、低情绪的累积效应具有显著的非对称性,在持续的高情绪累积下,市场中的资金会涌入波动水平低、流动性好的资产;而在持续的低情绪累积下,资金会选择投机性更强的资产。     

基于投资者注意力的证券市场传谣、辟谣效应分析

文章选取2014-2016年期间141家发布有效澄清公告的上市公司为样本,分析了“市场谣言”对股价的冲击影响以及官方“澄清公告”的市场反应,并从投资者注意力的角度,分析其对“传谣效应”和“辟谣效应”的反应.结果显示:投资者注意力资源配置量与传谣对股价的冲击正相关,但与“辟谣效应”的关系不明显.     

深圳股市股利分配效应的实证研究

本文采用市场模型分别对深市 A股高价股与低价股、大公司与小公司、不同时期发行股票以及不同地域股票的股利分配效应进行了实证研究 ,并分析其形成原因 ,在此基础上提出了对证券管理机关和上市公司以及投资者的建议     

国际金融危机传染机制的三阶段周期动态效应分析——基于VAR系统的实证检验

用金融危机三阶段周期理论剖析世界金融危机的传染机制,选择VAR系统进行检验,通过对9个国家Ⅰ危机隐蔽期、Ⅱ危机爆发期、Ⅲ危机深化期的摩根史丹利MSCI指数进行Granger因果关系检验及脉冲响应函数检验,结果发现:金融危机隐蔽期美国的金融动荡影响对澳大利亚外的7国金融市场呈现单向传染效应;金融危机爆发后,被传染金融危机的国家开始影响美国,呈现双向传染效应,证明危机的传染存在反馈机制;在金融全球化背景下的金融危机交叉传染,是国际危机程度不断加深的重要原因,因此,金融危机的冲击可以在全球范围内出现网状交叉感染.三时期的脉冲响应函数检验揭示了危机传染的动态效应,美国金融危机对其他国家的影响强度短期内很可能加大,持续时间比传统理论解析的过程相对延长.     

个体投资者羊群效应之成因

个体投资者在投资中,由于噪声影响和资金实力比较薄弱而处于信息劣势,往往会放弃自己的投资策略而模仿他人的行为策略,采取跟风产生羊群效应.本文对个体投资者羊群效应的两种表现形式模仿机构和其他个体投资者的羊群效应成因进行了详实的分析,解释了个体投资者羊群效应现象发生的原因.     

异质机构投资者的治理效应:基于高管薪酬视角

文章选取2004～2006年有机构投资者连续持股的A股上市公司为研究样本,在将机构投资者划分为压力敏感型和压力抵制型的基础上,从高管薪酬视角研究不同类型机构投资者的治理效应.     

基于农业视角的股票价格季节性效应实证研究

文章以农业上市公司为研究对象,运用最小二乘法和GARCH模型分析其股票价格变动是否存在异于其他行业的季节效应。结论显示：我国农业类上市公司股票价格收益率存在着冬季、夏季效应;考察其月份效应,发现存在着异于其他行业的二月、六月及十月效应;表现出了农业行业固有的特点,即受自然气候、农时规律的影响很大,其股票价格表现了显著的季节效应。     

贝叶斯非线性混合效应模型及其应用研究

由于常用的线性混合效应模型对具有非线性关系的纵向数据建模具有一定的局限性,因此对线性混合效应模型进行扩展,根据变量间的非线性关系建立不同的非线性混合效应模型,并根据因变量的分布特征建立混合分布模型。基于一组实际的保险损失数据,建立多项式混合效应模型、截断多项式混合效应模型和B样条混合效应模型。研究结果表明,非线性混合效应模型能够显著改进对保险损失数据的建模效果,对非寿险费率厘定具有重要参考价值。     

中国经济结构的演化及其增长效益的测度分析

 摘　　要:改革开放以来，中国在经济总量和人均收入持续提高的同时，经济结构也发生了巨大的变化。经济结构的变化不仅是经济发展的一部分，而且也是经济持续增长的一个重要动力和源泉。本文通过对Feder非均衡经济增长模型的拓展，建立了一个可同时测度经济结构变化的增长效益和投资数量及其效率的增长效益的非均衡计量经济模型。模型的分析表明，工业化和城市化的交互作用所导致的经济结构变动对中国近三十年来的经济增长具有巨大的推动作用，是推动中国经济持续高速增长的一个主要动力。模型的分析也表明，投资建设中的浪费和资本使用效率的低下，是目前中国经济发展过程中亟待解决的主要问题。经济发展不仅需要关注GDP的增长，更应该关注社会财富的增长和积累。     

Discrete Duration Models Combining Dynamic and Random Effects

Survivaldata may include two different sources of variation, namely variationover time and variation over units. If both of these variationsare present, neglecting one of them can cause serious bias inthe estimations. Here we present an approach for discrete durationdata that includes both time–varying and unit–specificeffects to model these two variations simultaneously. The approachis a combination of a dynamic survival model with dynamic time–varyingbaseline and covariate effects and a frailty model measuringunobserved heterogeneity with random effects varying independentlyover units. Estimation is based on posterior modes, i.e., wemaximize the joint posterior distribution of the unknown parametersto avoid numerical integration and simulation techniques, thatare necessary in a full Bayesian analysis. Estimation of unknownhyperparameters is achieved by an EM–type algorithm. Finally,the proposed method is applied to data of the Veteran's AdministrationLung Cancer Trial.     

政府债务、经济增长与非线性效应

本文基于61个国家1980-2009年的面板数据,采用系统GMM方法对政府债务与经济增长之间的非线性关系进行了实证检验,较好的克服了变量内生性问题。研究表明,政府债务与经济增长之间存在着非线性(倒U型)关系,这种关系普遍存在于发达国家和发展中国家;证明了债务阈值的存在性,且两组国家中政府债务阈值的大小存在差异。但是,政府债务阈值并不具有唯一性和确定性,它随利率、通货膨胀、经常账户和金融发展的变化而显示出动态性特征,且上述变量对两组国家的影响存在显著的区别。     

Random Effects Modeling and the Zero-Inflated Poisson Distribution

Overdispersion due to a large proportion of zero observations in data sets is a common occurrence in many applications of many fields of research; we consider such scenarios in count panel (longitudinal) data. A well-known and widely implemented technique for handling such data is that of random effects modeling, which addresses the serial correlation inherent in panel data, as well as overdispersion. To deal with the excess zeros, a zero-inflated Poisson distribution has come to be canonical, which relaxes the equal mean-variance specification of a traditional Poisson model and allows for the larger variance characteristic of overdispersed data. A natural proposal then to approach count panel data with overdispersion due to excess zeros is to combine these two methodologies, deriving a likelihood from the resulting conditional probability. In performing simulation studies, we find that this approach in fact poses problems of identifiability. In this article, we construct and explain in full detail why a model obtained from the marriage of two classical and well-established techniques is unidentifiable and provide results of simulation studies demonstrating this effect. A discussion on alternative methodologies to resolve the problem is provided in the conclusion.     

Random Effects,Fixed Effects and Hausman's Test for the Generalized Mixed Regressive Spatial Autoregressive Panel Data Model

This article suggests random and fixed effects spatial two-stage least squares estimators for the generalized mixed regressive spatial autoregressive panel data model. This extends the generalized spatial panel model of Baltagi et al. (2013 Baltagi, B. H., Egger, P., Pfaffermayr, M. (2013). A generalized spatial panel data model with random effects. Econometric Reviews 32:650–685.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) by the inclusion of a spatial lag term. The estimation method utilizes the Generalized Moments method suggested by Kapoor et al. (2007 Kapoor, M., Kelejian, H. H., Prucha, I. R. (2007). Panel data models with spatially correlated error components. Journal of Econometrics 127(1):97–130.[Crossref], [Web of Science ®] , [Google Scholar]) for a spatial autoregressive panel data model. We derive the asymptotic distributions of these estimators and suggest a Hausman test a la Mutl and Pfaffermayr (2011 Mutl, J., Pfaffermayr, M. (2011). The Hausman test in a Cliff and Ord panel model. Econometrics Journal 14:48–76.[Crossref], [Web of Science ®] , [Google Scholar]) based on the difference between these estimators. Monte Carlo experiments are performed to investigate the performance of these estimators as well as the corresponding Hausman test.     

社会网络的农地流转租金效应

社会网络对农地流转租金有着重要影响。结合安徽四县区1 010位农民田野调查数据,运用稳健回归方法和农地流转租金模型,考察了社会网络对农地流转租金的影响。结果表明:准强关系对农地流转租金有着正向显著影响,而强关系、准弱关系和弱关系均对农地流转租金有着负向影响。社会网络对于农地流转租金的影响主要表现为间接影响而非直接影响,其中准强关系对于农地流转租金有着间接正向影响,而准弱关系和弱关系对农地流转租金有着间接负向影响。     

Uncertain Health and Survival: Effects on End-of-Life Consumption

This article analyzes the impact of health and survival uncertainty on the saving and consumption decisions of retirees. A dynamic programming approach is used to model the household's planning problem. The utility parameters are estimated using panel data. We find that a fall into poor health raises the marginal utility from consumption. Simulations are used to indicate the effects of falling into poor health and loss of spouse. They reveal a large transfer from the healthy to the sick partner and a strong dependence of saving on the survivor benefits, suggesting that concern about the surviving spouse is an important motive for saving.     

Empirical Non-Parametric Control Charts: Estimation Effects and Corrections

Owing to the extreme quantiles involved, standard control charts are very sensitive to the effects of parameter estimation and non-normality. More general parametric charts have been devised to deal with the latter complication and corrections have been derived to compensate for the estimation step, both under normal and parametric models. The resulting procedures offer a satisfactory solution over a broad range of underlying distributions. However, situations do occur where even such a large model is inadequate and nothing remains but to consider non- parametric charts. In principle, these form ideal solutions, but the problem is that huge sample sizes are required for the estimation step. Otherwise the resulting stochastic error is so large that the chart is very unstable, a disadvantage that seems to outweigh the advantage of avoiding the model error from the parametric case. Here we analyse under what conditions non-parametric charts actually become feasible alternatives for their parametric counterparts. In particular, corrected versions are suggested for which a possible change point is reached at sample sizes that are markedly less huge (but still larger than the customary range). These corrections serve to control the behaviour during in-control (markedly wrong outcomes of the estimates only occur sufficiently rarely). The price for this protection will clearly be some loss of detection power during out-of-control. A change point comes in view as soon as this loss can be made sufficiently small.     

Direct and Indirect Causal Effects via Potential Outcomes*

Abstract. The use of the concept of ‘direct’ versus ‘indirect’ causal effects is common, not only in statistics but also in many areas of social and economic sciences. The related terms of ‘biomarkers’ and ‘surrogates’ are common in pharmacological and biomedical sciences. Sometimes this concept is represented by graphical displays of various kinds. The view here is that there is a great deal of imprecise discussion surrounding this topic and, moreover, that the most straightforward way to clarify the situation is by using potential outcomes to define causal effects. In particular, I suggest that the use of principal stratification is key to understanding the meaning of direct and indirect causal effects. A current study of anthrax vaccine will be used to illustrate ideas.