The CEO Performance Effect: Statistical Issues and a Complex Fit Perspective

How CEOs affect strategy and performance is important to strategic management research. We show that sophisticated statistical analysis alone is problematic for establishing the magnitude and causes of CEO impact on performance. We discuss three problem areas that substantially distort the measurement and sources of a CEO performance effect: (1) the nature of performance time series, (2) confounding and (3) the discovery of many interactions associated with the CEO performance effect. We show that the aggregate of empirical research implies complex interdependency as the driver of the CEO performance effect. This suggests a ‘fit’ model requiring new research approaches. Copyright © 2011 John Wiley & Sons, Ltd.     

驰名商标诉讼证据和纠纷解决的理论出路

驰名商标制度在中国所发生的异化、泛滥现象已经引起法律界对其未来演变的担忧。在对制度本源的追溯中,驰名商标显现了诉讼证据和纠纷解决的规制本意。这一本意应成为反思现状的理论基点,更是推进制度调整的核心动力和准则。在调整过程中,回归私权本质、市场主导,行政统一认定、法院司法审查,应当被预设为改革和修法的目标与方向。     

上海商学院校级重点课程“概率论与数理统计”建设探究

本文指出上海商学院校级重点课程"概率论与数理统计"建设的必要性,从转变教育观念、改变教学方法等方面提出了本课程建设的思路,并总结了该课程建设取得的初步成效。     

供电企业统计信息失真原因分析及对策

供电企业统计信息失真影响企业领导科学决策和企业经营计划、规划的编制,损害党和政府在人民群众中的形象和威信,须从营造企业和社会环境、加强各级人员的职业道德建设和业务培训等方面进行防止.     

Large deviation principles and loglog laws for observation processes

Let ( X_n, n ≥ 1) be an i.i.d. sequence of positive random variables with distribution function H . Let φ _H := {(n, X_n ), n ≥ 1) be the associated observation process. We view φ h as a measure on E := [0, ∞) ∞ (0, φ] where φ_H (A) is the number of points of φ _H which lie in A . A family ( V_s, s> 0) of transformations is defined on E in such a way that for suitable H the distributions of ( V_sφ_H, S > 0) satisfy a large deviation principle and that a related Strassen-type law of the iterated logarithm also holds. Some consequent large deviation principles and loglog laws are derived for extreme values. Similar results are proved for φ _H replaced by certain planar Poisson processes.     

Fifty years of measurement and scaling in the Dutch social sciences

It is evident from recent issues of journals like Psychometrika and Applied Psychological Measurement that the Dutch contribution to the development of the area of measurement and scaling is considerable. In the first part of this paper, we try to answer the question how this could happen. In the early development De Groot in Amsterdam and Van de Geer in Leiden created an academic climate that made it possible for many researchers to develop their skills. Some other possible reasons are sketched for the flourishing of this area in the Netherlands, but it is difficult to assess which reasons were most important. In the second part of the paper, we analyze the international impact of the Dutch contribution by a citation analysis in four fields of measurement and scaling: factor analysis, test theory including item response theory, latent class analysis, and optimal scaling and multidimensional scaling. It appears that Dutch researchers not only publish a lot, but also that the impact of their publications is at the same level as publications produced world-wide.     

Macro‐integration with inequality constraints: an application to the integration of transport and trade statistics

Macro‐integration is the process of combining data from several sources at an aggregate level. We review a Bayesian approach to macro‐integration with special emphasis on the inclusion of inequality constraints. In particular, an approximate method of dealing with inequality constraints within the linear macro‐integration framework is proposed. This method is based on a normal approximation to the truncated multivariate normal distribution. The framework is then applied to the integration of international trade statistics and transport statistics. By combining these data sources, transit flows can be derived as differences between specific transport and trade flows. Two methods of imposing the inequality restrictions that transit flows must be non‐negative are compared. Moreover, the figures are improved by imposing the equality constraints that aggregates of incoming and outgoing transit flows must be equal.     

Extremes and Robustness: A Contradiction?

Stochastic models play an important role in the analysis of data in many different fields, including finance and insurance. Many models are estimated by procedures that lose their good statistical properties when the underlying model slightly deviates from the assumed one. Robust statistical methods can improve the data analysis process of the skilled analyst and provide him with useful additional information. For this anniversary issue, we discuss some aspects related to robust estimation in the context of extreme value theory (EVT). Using real data and simulations, we show how robust methods can improve the quality of EVT data analysis by providing information on influential observations, deviating substructures and possible mis-specification of a model while guaranteeing good statistical properties over a whole set of underlying distributions around the assumed one.     

Nonparametric event study tests

This paper provides the first documentation of the power and specification of the generalized sign test, which is based on the percentage of positive abnormal returns in an estimation period. In simulations using daily stock return data, the generalized sign test is well specified with both exchange listed and NASDAQ stocks. A rank test is more powerful under ideal conditions. However, the rank test is more sensitive to increases in the length of the event window, to increases in return variance, and to thin trading. The generalized sign test is a viable alternative to the rank test under these conditions.