How virtual reality influences travel intentions: The role of mental imagery and happiness forecasting

Virtual reality can influence consumer behavior in powerful ways, but in the domain of tourism marketing, less is known about the psychological process. One possibility is that VR has stronger impact on mental imagery than traditional channels, creating vivid simulations of what it would be like to visit destinations. This process might create hedonic expectations of future happiness, which should increase the willingness to actually seek out the destination in real life. The results from a laboratory experiment provided support to this hypothesis. Participants were randomly assigned to view a nature destination either in traditional still images or virtual reality, and then reported their initial responses before making an actual consumer choice. VR exposure led to higher levels of mental imagery and happiness predictions, which in turn was associated with stronger travel intentions and purchasing decisions. However, VR effects on consumer choice were moderated by previous experience with the destination.     

中国生态城建设的现状特征与发展态势——中国百个生态城调查分析

我国近些年生态城建设呈现出数量多、规模大、发展速度快的特征,目前全国已出现一百多个大小不等的新建生态城项目。但这些生态城是否都是真正意义上的生态城受到学者和公众的质疑,因此迫切需要全面了解当前我国生态城建设的基本情况和特点,总结其发展态势。本研究针对我国生态城市建设实践,分析其类型、分布特征、产业特征和存在的问题,以期为制定生态城市发展政策提供依据。     

Combining forecasts: An application to elections

We summarize the literature on the effectiveness of combining forecasts by assessing the conditions under which combining is most valuable. Using data on the six US presidential elections from 1992 to 2012, we report the reductions in error obtained by averaging forecasts within and across four election forecasting methods: poll projections, expert judgment, quantitative models, and the Iowa Electronic Markets. Across the six elections, the resulting combined forecasts were more accurate than any individual component method, on average. The gains in accuracy from combining increased with the numbers of forecasts used, especially when these forecasts were based on different methods and different data, and in situations involving high levels of uncertainty. Such combining yielded error reductions of between 16% and 59%, compared to the average errors of the individual forecasts. This improvement is substantially greater than the 12% reduction in error that had been reported previously for combining forecasts.     

Playing the synthesizer with Canadian data: Adding polls to a structural forecasting model

Election forecasting has become a fixture of election campaigns in a number of democracies. Structural modeling, the major approach to forecasting election results, relies on ‘fundamental’ economic and political variables to predict the incumbent’s vote share usually a few months in advance. Some political scientists contend that adding vote intention polls to these models—i.e., synthesizing ‘fundamental’ variables and polling information—can lead to important accuracy gains. In this paper, we look at the efficiency of different model specifications in predicting the Canadian federal elections from 1953 to 2015. We find that vote intention polls only allow modest accuracy gains late in the campaign. With this backdrop in mind, we then use different model specifications to make ex ante forecasts of the 2019 federal election. Our findings have a number of important implications for the forecasting discipline in Canada as they address the benefits of combining polls and ‘fundamental’ variables to predict election results; the efficiency of varying lag structures; and the issue of translating votes into seats.     

Do analysts who understand accounting conservatism exhibit better forecasting performance?

This study investigates the performance of analysts when they match the asymmetric timeliness of their earnings forecast revisions (i.e., asymmetric forecast timeliness) with the asymmetric timeliness of firms’ reported earnings (i.e., asymmetric earnings timeliness). We find that better timeliness‐matching analysts produce more accurate earnings forecasts and elicit stronger market reactions to their forecast revisions. Further, better timeliness‐matching analysts issue less biased earnings forecasts, more profitable stock recommendations and have more favorable career outcomes. Overall, our results indicate that analysts’ ability to incorporate conditional conservatism into their earnings forecasts is an important reflection of analyst expertise and professional success.     

Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.     

Measuring and forecasting regional environmental and economic efficiency in Italy

Environmental and economic efficiency has being receiving growing attention among researchers. In general terms, this concept is related to the capability of the economic systems to employ natural resources efficiently, so as to increase economic and human wealth. This clearly implies that both the economic and ecological aspects of decisions ought to be considered. Bearing this in mind, this article considers economic and ecological performance together, by applying data envelopment analysis and the Malmquist productivity index to investigate the efficiency of the 20 Italian regions from 2004 to 2011. The results reveal that the northern regions have been more efficient than the southern ones, highlighting the strong geographical differences between the two. Furthermore, this article uses the grey system theory to forecast regional, economic and environmental efficiency. The results of the forecasting analysis show that the north–south duality remains strong and will possibly increase since the regions in the south get worse in term of environmental and economic efficiency.     

协同知识管理研究进展

近年来,协同知识管理是一个新的、值得关注的研究方向,为了加深对协同知识管理的梳理和便于今后对其进行深入研究,在试图对国内外协同知识管理相关研究作比较全面分析的基础上,对协同知识管理未来的发展趋势作了进一步的探讨,指出需要在隐显性知识协同管理模式、协同知识创新资源体系、知识管理的协同效应、知识融合与协同决策等方面作深入研究。     

Distributed ARIMA models for ultra-long time series

Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management. This paper develops a novel distributed forecasting framework to tackle the challenges of forecasting ultra-long time series using the industry-standard MapReduce framework. The proposed model combination approach retains the local time dependency. It utilizes a straightforward splitting across samples to facilitate distributed forecasting by combining the local estimators of time series models delivered from worker nodes and minimizing a global loss function. Instead of unrealistically assuming the data generating process (DGP) of an ultra-long time series stays invariant, we only make assumptions on the DGP of subseries spanning shorter time periods. We investigate the performance of the proposed approach with AutoRegressive Integrated Moving Average (ARIMA) models using the real data application as well as numerical simulations. Our approach improves forecasting accuracy and computational efficiency in point forecasts and prediction intervals, especially for longer forecast horizons, compared to directly fitting the whole data with ARIMA models. Moreover, we explore some potential factors that may affect the forecasting performance of our approach.     

基于重心法与物流量预测的物流园区选址

在研究物流园区选址理论的基础上,为使物流运输成本最小和物流园区的建设符合长远发展,在对物流园区选址时使用理论中的重心法并建立相应选址数学模型,将数学模型与趋势外推预测法预测的物流园区物流量结合,在定量的基础上计算得到物流园区的选址位置,同时为精确地得到选址位置开发了基于重心法的选址模型软件。最后通过吉木乃县物流园区选址说明该方法的可行性。