Inaccurate forecasts of the logistic growth model for Nobel Prizes

Modis [Technol. Forecast. Soc. Change 34 (1988) 95] reports that a logistic growth (LG) model of the number of U.S. Nobel Prize recipients provides an excellent fit for the period 1901-1987. This model forecasts that approximately 235 Americans will receive a Nobel Prize by year-end 2002 and that a total of 283 Americans will eventually receive a Nobel Prize. We use recent data (1901-2002) on prize recipients to provide a revised test of this model. The results of extensive holdout forecasting and nonlinear least-squares fits to the data provide convincing evidence that the LG model systematically underpredicts the number of Nobel Prizes awarded to Americans. For instance, the cumulative number of American recipients as of year-end 2002 is 270, significantly larger than the LG forecast of 235. We argue that other approaches to forecasting the number of future Nobel awards should be considered.     

Evaluation designs for technology assessments and forecasts

The worth of technology assessments and technological forecasts need not remain a matter of faith; indeed, it cannot. Evaluation of technology assessments and forecasts is indicated both to assess their effectiveness and to provide feedback useful for improving the state of the art. Drawing upon evaluation research, we present a range of alternative evaluation designs that fit into four general categories; pre-planned or post-hoc evaluations of single studies; pre-planned or post-hoc comparisons among multiple studies. We distinguish among three classes of performance objectives meriting evaluative attention: validity, utility, and advancement of the methodological art. For each objective we in turn consider pros and cons of several specific evaluation designs. We conclude with recommendations for two coordinated, but selective, evaluation programs, one focused on comparison among extrapolative forecasting techniques and one devoted to certain aspects of technology assessment methods and utilization.     

Multiple method forecasts for discontinuous innovations

This paper argues for the development of more explicit forecasting methodologies that use the pragmatics of combining methods and the philosophical base of multiple perspectives. The increasingly common “wicked” problem of forecasting demand for discontinuous innovations (DI) at the concept testing stage of new product development is used to ground the discussion. We look to the interpretivist group-based inquiry methodologies in the management and information systems literature, and coupled this with discussions with forecasting managers, to provide evidence to support the adoption of this approach. Relativism is briefly critiqued and the accuracy of the combining methods forecasting literature reviewed. It appears that the managers interviewed could benefit from an explicit understanding of the multiple perspective approach, as they already appeared to have appreciated the need for a broader based approach than traditional forecasting techniques. It is therefore hoped that as a result of this paper, more managers involved with the “wicked” problem of innovative product forecasting will recognise the need to adopt a more explicit multiple perspective inquiry methodology in their efforts to combine forecasting methods.     

Comparing linear and nonlinear forecasts for stock returns

We compare the out-of-sample performance of monthly returns forecasts for two indices, namely the Dow Jones (DJ) and the Financial Times (FT) indices. A linear and a nonlinear artificial neural network (ANN) model are used to generate the out-of-sample competing forecasts for monthly returns. Stationary transformations of dividends and trading volume are considered as fundamental explanatory variables in the linear model and the input variables in the ANN model. The comparison of out-of-sample forecasts is done on the basis of forecast accuracy, using the Diebold and Mariano test [J. Bus. Econ. Stat. 13 (1995) 253.], and forecast encompassing, using the Clements and Hendry approach [J. Forecast. 5 (1998) 559.]. The results suggest that the out-of-sample ANN forecasts are significantly more accurate than linear forecasts of both indices. Furthermore, the ANN forecasts can explain the forecast errors of the linear model for both indices, while the linear model cannot explain the forecast errors of the ANN in either of the two indices. Overall, the results indicate that the inclusion of nonlinear terms in the relation between stock returns and fundamentals is important in out-of-sample forecasting. This conclusion is consistent with the view that the relation between stock returns and fundamentals is nonlinear.     

诺贝尔奖与科学技术发展的社会环境

通过对“美国为什么‘盛产’诺贝尔奖得主”和“中国有利于重大科技创新的社会环境正逐步形成“这两个问题的分析,说明什么是促进科学技术发展的良好的社会环境。     

A rational foundation for wishful exchange rate forecasts

Professor Takatoshi Ito in a recent American Economic Review paper documents that exporters and importers have biased exchange rate forecasts and the biases are in opposite directions. At first glance, it is difficult to provide a rational foundation for such behavior. Professor Ito hypothesizes that these forecasts are the result of wishful expectations. However, if forecasts are stochastic, then minimization of a quadratic loss function results in a rational hedge applied to either $/¥ or ¥/$. The different perspectives of importers and exporters determines which form is hedged and determines the direction of the bias.     

Empirical confidence intervals for USDA commodity price forecasts

Conventional procedures for calculating confidence limits of forecasts generated by statistical models provide little guidance for forecasts based on a combination or a consensus process rather than formal models, as is the case with US Department of Agriculture (USDA) forecasts. This study applied and compared several procedures for calculating empirical confidence intervals for USDA forecasts of corn, soybean and wheat prices over the 1980/81 through 2006/07 marketing years. Alternative procedures were compared based on out-of-sample performance over 1995/96 through 2006/07. The results of this study demonstrate that kernel density, quantile distribution and best fitting parametric distribution (logistic) methods provided confidence intervals calibrated at the 80% level prior to harvest and 90% level after harvest. The kernel density-based method appears most accurate both before and after harvest with the final value falling inside the forecast interval 77% of the time before harvest and 92% after harvest, followed by quantile regression (73% and 91% before and after harvest, respectively) logistic distribution (73% and 90% before and after harvest, respectively) and histogram (66% and 84% before and after harvest, respectively). Overall, this study demonstrates that empirical approaches may be used to construct more accurate confidence intervals for USDA corn, soybean and wheat price forecasts.     

Competition for Power and Altruism

The paper analyzes the trade-off between power and altruism by using an experimental framework which involved a group of experimental agents, undergraduate students of the University of Siena. The results show that the introduction into the experimental structure of a tournament for the power appreciably altered the behaviour of agents. More specifically the degree of altruism, measured by the dictator offers, significantly decreased when the agents were able to trade altruism for power. The results were more clear-cut and robust in the case of the dictator game, but also in the case of the ultimatum game the introduction of the tournament for power altered the behavior of subjects. A significant gender effect emerged.     

对抗竞争·合作竞争·超越竞争

随着经济发展的不同阶段,企业的竞争也经历了不同的导向阶段。如今,各界对于激烈的对抗竞争已有了比较清晰的看法,提倡企业间采取合作竞争。为实现企业的持久优势,理论界也提出了超越竞争的理论。本文尝试分析从对抗竞争到合作竞争再到超越竞争这一发展主线各个阶段的优劣。     

Partial and system forecasts in scientometrics

Science itself can be considered as a “fuzzy system.” In attempting to deal with possible laws of scientific development we formulate a simple, partial model and illustrate its use as a means to control the strategy of investments in science.     

Real-time forecasts of economic activity for Latin American economies

Macroeconomic policy decisions in real-time are based on the assessment of current and future economic conditions. Crucially, these assessments are made difficult by the presence of incomplete and noisy data. The problem is more acute for emerging market economies, where most economic data are released infrequently with a (sometimes substantial) lag. This paper evaluates nowcasts and forecasts of real GDP growth using five models for ten Latin American countries. The results indicate the flow of monthly data helps to improve forecast accuracy, and the dynamic factor model consistently produces more accurate nowcasts and forecasts relative to other model specifications, across most of the countries we consider.     

Integration of scientific forecasts: Methodology for integration of scientific forecasts in the process of national science policy making

A systematic procedure of confrontation and integration of scientific forecasts is explained on the basis of practical experience of science policy making at the Czechoslovak Academy of Sciences. The output of the procedure is the science forecasting field and represents a relatively stable methodological form and structure of forecasting information, the mission of which is to mediate the communication between the world of science and the society. The methodology is based on the hypothesis that the traditional dichotomy between internal and external driving forces of the progress in scientific knowledge can be overcome through the confrontation and integration of outcomes of two systems of forecasting the societal utilization of science: the demand side and the supply side.Science forecasting serves as a tool for linking two sorts of scientific forecasts and for integrating their interrelationships into science policy decision situations at the strategic, tactical/programming, and operational/planning/management level.     

A cross-impact model applicable to forecasts for long-range planning

A methodology is developed, which is based on cross-impact analysis and which is useful in estimating the effects of uncertainty in future sales programs and the effects of program interaction on long-range sales forecasts. The utility of the cross-impact methodology vis-a-vis the conventional decision-tree approach lies in the reduction of complexity made possible when a large number of programs must be considered.     

诺贝尔经济学奖获得者William Vickrey追忆

WilliamVickrey，哥伦比亚大学经济学荣誉教授，于1996年10月荣获诺贝尔经济学奖(与英国剑桥大学的JamesA.Mirrlees教授共获)。三天后，因心脏病逝世。由于健康问题，Vickrey夫人不能乘坐飞机，因此，我代Vickrey先生去斯德哥尔摩，接受此项殊荣。自1935年我和Vickrey教授一起在纽约哥伦比亚大学学习经济学以来，我们一直是好朋友，作为朋友和同事，我们一直有着密切往来，在他事业的巅峰，他的突然离去，不仅让他的夫人，他亲密的朋友感到难过，对广大民众来说也是不幸之事，他再也不能用他过人的才智推广和倡导他所深信的能为人类带来…