Rise and fall of stars: Investigating the evolution of star status in professional team sports

This article describes an empirical study of the rise and fall of star athletes, using data from the National Basketball Association (NBA) from 1987 to 2008. We measure star status by the number and share of all-star votes, and we apply both Tobit regression and hazard models to investigate the determining factors for star status. We find that the attainment of star status begins with the athlete's exceptional individual performance. We also find that having won a championship in the past can have a long-lasting effect on a player's popularity. The popularity of an athlete depends on his team in two ways: the attainment of star status is associated with strong team performance, and star teammates can reinforce each other's popularity. Interestingly, whereas stars can move from a losing team to a winning team to extend their star life, a team change can be very risky for new stars. Our results also suggest that teams with a large fan base, winning records, and star players should leverage these assets in attracting and retaining star players.     

出口贸易与自主创新——基于我国制造业企业的实证研究

本文从出口贸易与自主创新的视角,对出口是否促进了我国本土制造业企业的自主创新进行了系统考察,并提出了理论假设。借助于1999-2003年和2005-2007年间我国制造业企业的微观数据,采用Tobit模型,就我国制造业企业的出口行为是否具有"出口中学习"、是否具有促进企业自主创新的作用效应进行了实证检验,为转型时期出口导向发展战略能否促进我国经济可持续增长提供了一个可供识别的微观基础。研究发现在样本观察期内,企业的出口行为会对其自主创新活动产生复杂的影响。其中,企业规模是影响效果的关键因素,规模越大的企业出口的促进作用越明显,越小的企业出口反而具有抑制作用。同时,2005年以来的出口贸易政策的调整对自主创新具有积极的推动作用。更进一步,按产业特性对比研究两个样本期内企业出口行为,发现不同产品特性的产业出口与自主创新关系存在显著差异,这就为从更深入角度全面考察企业出口与自主创新之间可能具有的复杂相互关系。     

PORTFOLIO SELECTION PROBLEMS VIA THE BIVARIATE CHARACTERIZATION OF STOCHASTIC DOMINANCE RELATIONS1

Stochastic dominance (SD) is a very useful tool in various areas of economics and finance. the purpose of this paper is to provide the results of SD relations developed in other areas such as applied probability which, we believe, are useful for many portfolio selection problems. In particular, the bivariate characterization of SD relations given by Shanthikumar and Yao (1991) is a powerful tool for the demand and the shift effect problems in optimal portfolios. the method enables one to extend many results that hold for the case where the underlying lying assets are statistically independent to the dependent case directly.     

International evidence on the impact of regulations and supervision on banks’ technical efficiency: an application of two-stage data envelopment analysis

This study uses a sample of 715 banks from 95 countries and two-stage data envelopment analysis (DEA) to provide international evidence on the impact of regulations and supervision approaches on banks’ efficiency. We first use DEA to estimate technical and scale efficiency. We then use Tobit regression to investigate the impact of several regulations related to capital adequacy, private monitoring, banks’ activities, deposit insurance schemes, disciplinary power of the authorities, and entry into banking on banks’ technical efficiency. We estimate several specifications while controlling for bank-specific attributes and country-level characteristics accounting for macroeconomic conditions, financial development, market structure, overall institutional development, and access to banking services. In several cases, the results provide evidence in favour of all three pillars of Basel II that promote the adoption of strict capital adequacy standards, the development of powerful supervisory agencies, and the creation of market disciplining mechanisms. However, only the latter one is significant in all of our specifications. While the remaining regulations do not appear to have a robust impact on efficiency, several other country-specific characteristics are significantly related to efficiency.

基于超效率DEA和Tobit模型的中国上市煤炭企业经营效率现状分析

本文运用数据包络分析（DEA）方法和Tobit回归模型,首先选取2011年沪深两市具有代表性的18家煤炭上市公司为研究对象,以货币资金、流动资产合计、长期股权投资、固定资产和非流动资产合计为投入变量,主营业务收入、主营业务利润、利润总额为产出变量,运用CCR模型从技术效率、纯技术效率和规模效率等方面对其经营效率进行评价和分析;其次运用超效率DEA模型对CCR模型下DEA有效的决策单元计算其超效率值,从而为所有决策单元提供完整的效率值排序;最后,本文运用Tobit模型,分析影响中国上市煤炭企业经营效率的重要因素,为决策者提供提高企业经营效率的方向和政策。     

How Much of the Gender Difference in Child School Enrolment Can Be Explained? Evidence from Rural India

There are significant gender differences in child schooling in the Indian states though very few studies explain this gender difference. Unlike most existing studies we take account of the implicit and explicit opportunity costs of schooling and use a bivariate probit model to jointly determine a child's participation in school and market jobs. Results obtained from the World Institute of Development Economics Research (WIDER) villages in West Bengal suggest that indicators of household resources, parental preferences, returns to and opportunity costs of domestic work significantly affect child school enrolment. While household resources have similar effects on enrolment of boys and girls, other arguments tend to explain a part of the observed gender difference. Even after taking account of all possible arguments, there remains a large variation in gender differences in child schooling that cannot be explained by differences in male and female characteristics in our sample.     

Return threshold model analysis of two stock markets： Evidence study of Italy and Germany＇s stock returns

This paper discusses the model construction and the association between the Italy and the Germany＇s stock markets. The period of study data is from January 3, 2000 to June 30, 2008. This paper also utilizes Student＇s t distribution to analyze the proposed model. The empirical results show that the two stock markets are mutually affected each other, and the dynamic conditional correlation （DCC） and the bivariate asymmetric-GARCH （1, 2） model is appropriate in evaluating the relation between them. The empirical result also indicates that Italy and Germany＇s stock markets show a positive relationship. The average value of correlation coefficient equals to 0.8424, which implies that the two stock markets return volatility have a synchronized influence on each other. In addition, the empirical result also shows that there is an asymmetrical effect between Italy and the Germany＇s stock markets, and demonstrates that the good news and bad news of the stock returns＇ volatility will produce the different variation risks for Italy and the Germany＇s stock price markets.