Estimating a generalized correlation coefficient for a generalized bivariate probit model

In this paper we consider semiparametric estimation of a generalized correlation coefficient in a generalized bivariate probit model. The generalized correlation coefficient provides a simple summary statistic measuring the relationship between the two binary decision processes in a general framework. Our semiparametric estimation procedure consists of two steps, combining semiparametric estimators for univariate binary choice models with the method of maximum likelihood for the bivariate probit model with nonparametrically generated regressors. The estimator is shown to be consistent and asymptotically normal. The estimator performs well in our simulation study.     

Measuring volatility with the realized range

Realized variance, being the summation of squared intra-day returns, has quickly gained popularity as a measure of daily volatility. Following Parkinson [1980. The extreme value method for estimating the variance of the rate of return. Journal of Business 53, 61–65] we replace each squared intra-day return by the high–low range for that period to create a novel and more efficient estimator called the realized range. In addition, we suggest a bias-correction procedure to account for the effects of microstructure frictions based upon scaling the realized range with the average level of the daily range. Simulation experiments demonstrate that for plausible levels of non-trading and bid–ask bounce the realized range has a lower mean-squared error than the realized variance, including variants thereof that are robust to microstructure noise. Empirical analysis of the S&P500 index-futures and the S&P100 constituents confirms the potential of the realized range.     

Econometric analysis of copyrights

This paper explores the impact of copyrights on firm value and on the demand for firm output. Using panel data on franchise value and ticket sales from the National Football League over the 1991–2000 period, we analyze the effect of copyrights (in this case, team logos) using several parametric estimators, the Arellano and Bond [1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277–297] dynamic panel data estimator, and a semi-non-parametric method based on difference-in-differences propensity score matching. We find a negative effect of logo changes on franchise value that is robust across multiple specifications. In addition, logo changes also appear to have a moderate positive, albeit not particularly robust, impact on ticket sales.     

B2C电子商务物流配送中心选址模型研究

分析了B2C电子商务的发展趋势、发展瓶颈即物流配送成本,为优化物流成本,构建了基于不同路段运输单价不同的最优运输费用的物流配送中心选址模型.     

高校网上招生系统的后续开发与研究

针对目前院校端网上招生录取系统存在的局限性,文章详细介绍了高校网上招生后续开发管理系统的功能,讨论了系统实现中的关键技术及实现方法,指出该系统采用分布式计算技术,建立基于组件的3层C/S模型的体系结构,具有高效性、可伸缩性、开发周期短、安全性高的特点。     

Java语言的特点与C^＋＋语言的比较

Java语言是一门很优秀的语言,具有面向对象、与平台无关、安全、稳定和多线程等优良特性,是目前软件设计中极为健壮的编程语言.Java已成为网络时代最重要的语言之一.与C++语言比较,Java是一种更纯粹的面向对象程序设计语言.     

将Python嵌入到C/C++应用程序中的编程方法

Python是一种面向对象的语言.本文介绍了怎样在不同的层次上将Python编译器作为一种扩展语言嵌入到用C/C++编写的应用程序之中.嵌入了Python的C/C++应用程序可以完成某些Python特有的功能.文章也讲述了如何扩展嵌入的Python使其获得应用程序本身的功能.最后,讲述了怎样使链接到静态库的应用程序获得动态扩展.     

B to C交易中消费者信任的生成机理研究

基于TRA、TPB和ETC等理论的扩展模型和Mcknight等人对信任的系统研究，构建出信任生成机理基础框架。同时，参考网上信任生成的周期说、因素说和DTR模型，提出由五条主线、一定的交互关系和大量影响因素组成的网上消费者信任生成机理综合模型，以期为理论研究和实践应用提供借鉴。     

审计费用的决定因素——来自我国B股上市公司的数据分析

审计费用的决定因素一直是国外会计审计学界关注的热点问题之一。基于我国B股上市公司数据，以2001年～2003年我国B股的上市公司为样本，对境外审计师的审计收费展开研究可发现：2001年度审计费用的决定因素并不明显，且与事务所规模的关系不显性；在2002年和2003年，审计费用与总资产和事务所规模成显性相关。究其原因，主要是声誉机制、信息披露制度和我国审计市场竞争激烈等因素所致。     

价值型投资在中国证券市场上的有力证据

本文给出了1994年至2005年间价值型投资在中国A股市场中的有力证据。Fama—MacBeth回归表明在中国A股市场中存在着显著的市盈率和市净率溢价，1994年至2005年中牛市和熊市的滚动策略显示了我国A股市场中的价值效应，并且风险和收益之间基本上呈现出收益大、风险大的特征。本文的实证研究结果将有助于中小投资者以及机构投资者的长期投资决策。