Forecasting the Adoption of GM Oilseed Rape: Evidence from a Discrete Choice Experiment in Germany

This paper explores farmers’ willingness to adopt genetically modified (GM) oilseed rape prior to its commercial release and estimates the ‘demand’ for the new technology. The analysis is based upon choice experiments with 202 German arable farmers. A multinomial probit estimation reveals that GM attributes such as gross margin, expected liability from cross pollination, or flexibility in returning to conventional oilseed rape significantly affect the likelihood of adoption. Neighbouring farmers’ attitudes towards GM cropping and a number of farmer and farm characteristics were also found to be significant determinants of prospective adoption. Demand simulations suggest that adoption rates are very sensitive to the profit difference between GM and non‐GM rape varieties. A monopolistic seed price would substantially reduce demand for the new technology. A monopolistic seed supplier would reap between 45% and 80% of the GM rent, and the deadweight loss of the monopoly would range between 15% and 30% of that rent. The remaining rent for farmers may be too small to outweigh possible producer price discounts resulting from the costs of segregating GM and non‐GM oilseed rape along the supply chain.     

From passenger growth to aircraft movements

Airlines are able to deal with passenger growth by either increasing the frequency or the aircraft size, which may entail different numbers of aircraft movements. Forecasting the latter is necessary for evaluating technologies, approaching future emissions or anticipating capacity constraints. Purpose of this paper is to forecast a typical fleet mix and the growth of aircraft movements on flight segments worldwide based on an assumed passenger growth. The methodology is implemented in a model called Forecast of Aircraft Movements. Basic approach is the assignment of each flight segment worldwide to a distance, passenger number and aircraft category. For each combination of distance and passenger numbers a typical fleet mix is defined. The forecasted worldwide growth of passenger demand and the empirically determined fleet mix is applied to all segments in order to derive a future scenario. Assuming a certain seat load factor, the frequency growth can be deduced from the aircraft movements generated for all segments. The paper includes a forecast for aircraft movements in a future scenario based on real schedule and passenger data and gives a detailed overview of the methodology and results considering airlines' behaviour.     

What do professional forecasters actually predict?

This paper studies what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into trend, business cycle, and irregular components. We examine which components are captured by professional forecasters by regressing their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods, we find that, in the short run, the Survey of Professional Forecasters can predict almost all of the variation in the time series due to the trend and the business cycle, but that the forecasts contain little or no significant information about the variation in the irregular component.     

Forecasting turning points in tourism growth

Tourism demand exhibits growth cycles, and it is important to forecast turning points in these growth cycles to minimise risks to destination management. This study estimates logistic models of Hong Kong tourism demand, which are then used to generate both short- and long-term forecasts of tourism growth. The performance of the models is evaluated using the quadratic probability score and hit rates. The results show that the ways in which this information is used are crucial to the models’ predictive power. Further, we investigate whether combining probability forecasts can improve predictive accuracy, and find that combination approaches, especially nonlinear combination approaches, are sensitive to the quality of forecasts in the pool. In addition, model screening can improve forecasting performance.     

基于效应的随机灰规划模型和应用

为了解决同时含有随机因素和灰色因素的不确定规划问题,通过结合区间灰数所属区间两个端点的随机性,给出随机区间灰数和随机区间灰函数的定义,提出了随机灰规划模型。通过综合效应函数理论用随机变量期望值和方差综合量化表示灰数所属区间的两个端点值。应用该理论对综合量化后的两个端点值继续进行综合量化,从而将随机灰规划转化为确定型规划问题。应用遗传算法进行求解。通过综合效应函数的理念,综合随机变量的期望和方差,同时综合区间灰数的区间因素,将随机灰规划数学模型转化为确定型规划模型即基于效应的随机灰规划模型。通过选取不同的综合效应函数,得到了关于不同决策意识下的随机灰规划的最优解。这个方法可为决策者进行不确定决策提供参考。     

A novel decision support model for satisfactory restaurants utilizing social information: A case study of TripAdvisor.com

Decision support models for satisfactory restaurants have attracted numerous researchers' attention. Many extant models do not consider the active, neutral and passive information in online reviews all at once. Moreover, they ignore the effect of interdependence among criteria on tourists' decision-making. To cover these defects, this study proposes a restaurant decision support model using social information for tourists on TripAdvisor.com. The model introduces fuzzy sets to denote online reviews and utilizes Bonferroni mean to consider interdependence among criteria. Furthermore, it uses a novel similarity measurement which can handle sparse data in fuzzy environments. To validate the model, we conduct a case study of TripAdvisor.com which compares the proposed model with four other models. The performance of each model is evaluated by the metric called the mean absolute error. The study shows that the proposed model can effectively support tourists' decision-making and it performs better than the other four models.     

Fuzzy Rasch model in TOPSIS: A new approach for generating fuzzy numbers to assess the competitiveness of the tourism industries in Asian countries

This study proposes a novel approach, the Fuzzy Rasch model, which combines Item Response Theory (IRT) and fuzzy set theory. This paper applies the Fuzzy Rasch model in Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to analyse the Tourism Destination Competitiveness (TDC) of nine Asian countries: China, Hong Kong, Japan, Korea, Malaysia, Singapore, Taiwan, Thailand and the Philippines. The study was conducted in 2009 using 6 criteria and 15 indices. The results demonstrate the feasibility of applying the Fuzzy Rasch model in TOPSIS to analyse TDC in Asian countries. In addition, the proposed model also provides an effective means of applying the MCDM method to study TDC. Furthermore, in 2009, the Asian countries were ranked from most to least competitive as follows: China, Japan, Hong Kong, Malaysia, Thailand, Singapore, Taiwan, Korea and the Philippines.     

中国总量性非自愿失业模型生成机制研究——基于凯恩斯就业理论的视角

中国失业问题主要不是结构问题,而是总量问题。运用凯恩斯就业理论中的周期性总量性失业模型。对长期总量性非自愿失业的生成机制的研究发现,应从控制人口增长、降低劳动参与率、缩短劳动时间及实行灵活的就业制度等方面扩张潜在就业规模,从而有效治理失业问题。促进社会的和谐发展。     

Electricity price forecasting: A review of the state-of-the-art with a look into the future

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures, and (iii) statistical testing of the significance of one model’s outperformance of another.     

经济地理与外资企业的区位选择

本文利用2004～2008年我国546类制造行业的省级面板数据,从新经济地理学的视角,研究了外资企业进入中国各省市区的决定因素。结果表明,各地的市场潜力、贸易成本、地理位置等新经济地理的因素对现阶段外资企业的区位选择有显著的影响,而传统FDI理论所关注的因素如工资成本、优惠政策等对外资企业的区位选择影响不显著。另外,本文还发现了各地区间还普遍存在着不同程度的市场分割现象,这会阻碍外资企业和FDI在省际的自由流动。这些结论对我国各地区今后有针对性地实施吸引外资企业和FDI的政策有一定借鉴意义。