中国进口与经济增长关系的区域差异分析

利用面板数据对中国东、中、西部的进口额和GDP增长关系进行的实证研究。在短期内，GDP增长对进口额影响最小是中部地区，在长期，西部GDP增长对进口额影响最大。中国经济增长对拉动世界经济增长起了十分重要的作用，中国经济增长减速也会影响全球经济。     

RESEARCH NOTE: ERRORS IN THE OCTOBER HOUSEHOLD SURVEY 1994 AVAILABLE FROM THE SOUTH AFRICAN DATA ARCHIVE1

The on‐line electronic documentation supplied with the 1994 October Household Survey by the South African Data Archive (SADA) appears to be incorrect. In particular, the electronic version of the questionnaire does not correspond to the hard copy in the possession of the author. The most serious error is that the race classification in the electronic copy is different from the classification on the hard copy. Researchers relying on the electronic copy will erroneously interchange the categories “Coloured”, “White” and “Black”. This could lead to seriously misleading analyses. The reason for this mistake can probably be attributed to a retyping of the questionnaire using the 1993 OHS as a template.     

Web数据挖掘技术探索与应用

Web数据挖掘是当今世界上的热门研究领域,本文从Web数据挖掘的定义开始分析研究Web数据挖掘的难点、过程等相关技术,并计论其在电子商务领域中的相关应用。     

Multiple generation product life cycle predictions using a novel two-stage fuzzy piecewise regression analysis method

Product life cycle (PLC) prediction plays a crucial role in strategic planning and policy definition for high-technology products. Forecast methodologies which can predict PLCs accurately can help to achieve successful strategic decision-making, forecasting, and foresight activities in high-technology firms, research institutes, governments, and universities. Over the past few decades, even though analytic framework strategies have been proposed for production, marketing, R&D (research and development), and finance, aiming at each stage of PLCs, forecast methodologies with which to predict PLCs are few. The purpose of this research is to develop a novel forecast methodology to allow for predictions of product life time (PLT) and the annual shipment of products during the entire PLC of multiple generation products. A novel two-stage fuzzy piecewise regression analysis method is proposed in this paper. In the first stage, the product life-time of the specific generation to be analyzed will be predicted by the fuzzy piecewise regression line that is derived based upon the product life-time of earlier generations. In the second stage of the forecast methodology, the annual shipment of products of the specified generation will be predicted by deriving annual fuzzy regression lines for each generation, based upon the historical data on the earlier generations' products. An empirical study predicting the life-time and the annual shipment of the 16 Mb (Mega bit) DRAM (Dynamic Random Access Memory) PLC is illustrated to validate the analytical process. The results demonstrate that two-stage fuzzy piecewise regression analysis can predict multiple generation PLT and PLC precisely, thereby serving as a foundation for future strategic planning, policy definitions and foresights.     

On the Role of Weight Restrictions in Data Envelopment Analysis

This paper examines the role that weight restrictions play in Data Envelopment Analysis (DEA). It is argued that the decision to include a factor (input or output) in a DEA model represents an implicit judgement that the factor has a non-trivial weight. It therefore seems perverse to allow DEA to assign a trivial weight to that factor in assessing the efficiency of a unit. There is therefore a strong case for imposing restrictions on factor weights. However, many existing methods of weight restriction are in practice unwieldy. This paper proposes an alternative approach we term contingent weight restriction which is both practical and intellectually consistent with the DEA philosophy. The paper explores the implications of alternative methods of weight restriction using simulated data from a well known production process.     

Productive Efficiency of the Swine Industry in Hawaii: Stochastic Frontier vs. Data Envelopment Analysis

Improving productive efficiency is an increasingly important determinant of the future of the swine industry in Hawaii. This paper examines the productive efficiency of a sample of swine producers in Hawaii by estimating a stochastic frontier production function and the constant returns to scale (CRS) and variable returns to scale (VRS) output-oriented DEA models. The technical efficiency estimates obtained from the two frontier techniques are compared. The scale properties are also examined under the two approaches. The industry's potential for increasing production through improved efficiency is also discussed.     

Lower Bound Restrictions on Intensities in Data Envelopment Analysis

We propose an extension to the basic DEA models that guarantees that if an intensity is positive then it must be at least as large as a pre-defined lower bound. This requirement adds an integer programming constraint known within Operations Research as a Fixed-Charge (FC) type of constraint. Accordingly, we term the new model DEA_FC. The proposed model lies between the DEA models that allow units to be scaled arbitrarily low, and the Free Disposal Hull model that allows no scaling. We analyze 18 datasets from the literature to demonstrate that sufficiently low intensities—those for which the scaled Decision-Making Unit (DMU) has inputs and outputs that lie below the minimum values observed—are pervasive, and that the new model ensures fairer comparisons without sacrificing the required discriminating power. We explain why the low-intensity phenomenon exists. In sharp contrast to standard DEA models we demonstrate via examples that an inefficient DMU may play a pivotal role in determining the technology. We also propose a goal programming model that determines how deviations from the lower bounds affect efficiency, which we term the trade-off between the deviation gap and the efficiency gap.     

Selecting Sites for New Facilities Using Data Envelopment Analysis

This paper develops a mathematical programming model for obtaining a best set of sites for planned facilities. The model is concerned with those situations where resource constraints are present. The specific setting for the paper involves the selection of sites for a set of retail outlets, wherein the ratio of aggregate outputs to inputs for the selected set is maximal among all possible sets that could be chosen. At the same time, the model guarantees that the only sets of stores allowable are those for which the available resources are used to the maximum extent possible.     

In at the deep end: Conducting processual research on organisational change

This article provides a series of reflections on the practice of carrying out processual research on organisational change. At a broad level, some of the main tasks associated with conducting company case studies are described and the benefits of this approach for dealing with complex change data are outlined. At a more specific level, the article addresses three main areas tied to the actual “doing” of processual research. First, the notion of tacit knowledge and “getting your hands dirty” by engaging in ongoing in-depth fieldwork. Second, the design and implementation of a longitudinal case study research programme. Third, the advantages and concerns of combining a range of different data collecting techniques in carrying out processual studies. Overall, the main intention is to provide some useful reflections and practical insights, as well as providing something of the flavour of carrying out this type of research.     

The Impact of Non-Normality and Estimation Methods in SEM on Satisfaction Research in Marketing

This paper discusses consequences of violating the normal distribution assumption imbedded in Structural Equation Modeling (SEM). Based on real data from a large sample customer satisfaction survey we follow the procedures as suggested in leading textbooks. We document consequences of this practice and discuss its impact on decision making in marketing.