A new method for measuring congestion in data envelopment analysis

In data envelopment analysis (DEA), there are two principal methods for identifying and measuring congestion: Those of Färe et al. [Färe R, Grosskopf S. When can slacks be used to identify congestion. An answer to W. W. Cooper, L. Seiford, J. Zhu. Socio-Economic Planning Sciences 2001;35:1–10] and Cooper et al. [Cooper WW, Deng H, Huang ZM, Li SX. A one-model approach to congestion in data envelopment analysis. Socio-Economic Planning Sciences 2002;36:231–8]. In the present paper, we focus on the latter work in proposing a new method that requires considerably less computation. Then, by proving a selected theorem, we show that our proposed methodology is indeed equivalent to that of Cooper et al.     

Characteristics of technology transfer in business ventures: the case of Daejeon, Korea

This article explores the characteristics of venture business and entrepreneurs in Korea to (1) identify technology transfer activities, (2) analyze the differences between technology transfer in linear and nonlinear venture businesses, and (3) guide more effective venture business policy and strategy. This empirical assessment reveals that entrepreneurs have insightful evaluations about their resources and capacities as well as expectations with regard to functions and features of science parks and incubators. Respondents from “linear model”-based start-ups tend to be older and have higher education, employ more basic research and development (R&D) and have more R&D-oriented careers, and have more varied work experience than “nonlinear”-based start-ups. The functions and features of science parks and incubators were generally not considered a critical influence on start-ups nor on the growth of venture businesses. Accordingly, alternative venture-nurturing strategies are discussed as being key to accelerate venture businesses growth.     

线性代数双语教学模式实现途径研究

分析了线性代数双语教学的必要性与可行性,探讨了适合我校的教学模式,给出了教学目标和评价方法。     

Asymptotics for Autocovariances and Integrated Periodograms for Linear Processes Observed at Lower Frequencies

One of the most frequently used class of processes in time series analysis is the one of linear processes. For many statistical quantities, among them sample autocovariances and sample autocorrelations, central limit theorems are available in the literature. We investigate classical linear processes under a nonstandard observation pattern; namely, we assume that we are only able to observe the linear process at a lower frequency. It is shown that such observation pattern destroys the linear structure of the observations and leads to substantially different asymptotic results for standard statistical quantities. Central limit theorems are given for sample autocovariances and sample autocorrelations as well as more general integrated periodograms and ratio statistics. Moreover, for specific autoregressive processes, the possibilities to estimate the parameters of the underlying autoregression from lower frequency observations are addressed. Finally, we suggest for autoregressions of order 2 a valid bootstrap procedure. A small simulation study demonstrates the performance of the bootstrap proposal for finite sample size.     

Disciplinary sanctions in English Premiership Football: Is there a racial dimension?

This paper assesses the evidence for a racial difference in both the dispensation of formal disciplinary sanctions and in the number of fouls called by referees in professional football. The study uses a unique dataset comprising player match-level information drawn from five recent seasons of the English Premiership. These data were merged with data from other sources to identify, among other things, the racial affiliation of the player across four separate categories (viz., white, black, mixed race, and Asian). No systematic evidence of an unfair treatment of players from the non-white minority groups in respect of either the receipt of disciplinary cards or in the number of penalised fouls called by referees was detected.     

Linear Programming Models for the Measurement of Environmental Performance of Firms—Concepts and Empirical Results

I use linear programming models to define standardised, aggregate environmental performance indicators for firms. The best practice frontier obtained corresponds to decision making units showing the best environmental behaviour. Results are obtained with data from U.S. fossil fuel-fired electric utilities, starting from four alternative models, among which are three linear programming models that differ in the way they account for undesirable outputs (pollutants) and resources used as inputs. The results indicate important discrepancies in the rankings obtained by the four models. Rather than contradictory, these results are interpreted as giving different, complementary kinds of information, that should all be taken into account by public decision-makers.     

Regulated absolute ruin problem with interest structure and linear dividend barrier

The uncontrolled surplus of an insurance company is a classical risk model. Now the risk model includes three features, namely debit interest, short-term and long-term invested interest, and linear dividend barrier. In this paper, the PDMP method and martingales are used for solvency studies in the risk model under regulation of minimum cash requirement. The integro-differential equations are derived for the expected discounted dividends under absolute ruin. In the case of exponential claim amounts, explicit expressions are obtained, as well as the numerical illustrations and their economic interpretation.     

一种改进的不完全指数复制方法

本文在考虑交易成本和投资组合动态调整的基础上,建立混合整数线性规划模型,引入内核搜索分析框架进行近似求解,并利用沪深300进行实证研究。研究发现,一是相比于基本内核搜索法,增强型内核搜索法仅在基准指数成分股数量很大时才会较大幅度提高求解质量;二是考虑投资组合动态调整的模型不仅更稳健,而且跟踪的继承性和保持性更好,尤其适用于单边市场;三是过度刻画现实交易特征一定程度上会降低不完全指数复制模型的复制和预测效果。     

Excel与SPSS在线性回归预测中的比较分析

线性回归分析是经济管理与预测的一个重要的量化分析方法。分别运用Excel与SPSS对同一实际问题进行线性回归分析,得出两种软件在线性回归预测问题上各有其优势,在经济管理的实际应用中无论选择那种软件都要了解其功能和操作的特点,有助于对实际问题恰当而且正确地分析与预测。     

Forecasting US consumer price index: does nonlinearity matter?

The objective of this article is to predict, both in sample and out of sample, the consumer price index (CPI) of the US economy based on monthly data covering the period of 1980:1–2013:12, using a variety of linear (random walk (RW), autoregressive (AR) and seasonal autoregressive integrated moving average (SARIMA)) and nonlinear (artificial neural network (ANN) and genetic programming (GP)) univariate models. Our results show that, while the SARIMA model is superior relative to other linear and nonlinear models, as it tends to produce smaller forecast errors; statistically, these forecasting gains are not significant relative to higher-order AR and nonlinear models, though simple benchmarks like the RW and AR(1) models are statistically outperformed. Overall, we show that in terms of forecasting the US CPI, accounting for nonlinearity does not necessarily provide us with any statistical gains.