基于分形理论的铁路客货运量预测

在阐述分形基本理论和分析铁路运输时间序列分形特性的基础上,基于变维分形理论对铁路客货运量进行预测。根据铁路运量分形预测原理及步骤,以全国铁路运量预测为例进行分析,计算得到铁路2012年、2015年、2020年的客货运量,以及2010—2020年的客货运量增长率,并对预测结果进行了具体分析。     

Evaluating the conditionality of judgmental forecasts

We propose a framework for evaluating the conditionality of forecasts. The crux of our framework is the observation that a forecast is conditional if revisions to the conditioning factor are incorporated faithfully into the remainder of the forecast. We consider whether the Greenbook, Blue Chip survey and Survey of Professional Forecasters exhibit systematic biases in the manner in which they incorporate interest rate projections into the forecasts of other macroeconomic variables. We do not find strong evidence of systematic biases in the three economic forecasts that we consider, as the interest rate projections in these forecasts appear to be incorporated efficiently into the forecasts of other economic variables.     

Are more able managers good future tellers? Learning from Japan

Baik et al. (2011) find that high-ability managers in the U.S. are more likely to issue accurate management earnings forecasts. Focusing on Japan, where management earnings forecasts are effectively mandated, we extend the literature by exploring (1) whether the relationship between managerial ability and forecast accuracy is unique to the U.S. disclosure system, where management forecasts are voluntary, and (2) how high-ability managers increase their forecast accuracy. We find that managerial ability is negatively associated with forecast errors based on initial forecasts, suggesting that high-ability managers are more likely to issue accurate forecasts at the beginning of the fiscal year. We then show that high-ability managers are less likely to revise their initial earnings forecasts and less likely to use earnings management to improve the accuracy of their earnings forecasts. Our findings show that, while high-ability managers are more likely to issue accurate initial management forecasts, low-ability managers are more likely to revise their forecasts and conduct earnings management to reduce their forecast errors.     

USER INFLUENCE ON THE RELATIONSHIP BETWEEN FORECAST ACCURACY,APPLICATION AND LOGISTICS PERFORMANCE

When companies develop and apply more accurate forecasts in their planning and management activities, they have the potential to improve performance throughout their organization and across the supply chain. To realize these improvements, however, companies must (1) implement techniques and practices that improve forecast accuracy, and (2) integrate the more accurate forecasts into their planning and management activities. While much research has focused on accomplishing the first of these requirements, few studies have investigated the important role that user's play in the application of forecasts for logistics planning and management. This article establishes a connection between forecast performance, user perceptions of the quality of forecasts they receive, the extent that they use the forecasts and the resulting impact on logistics performance.     

Nowcasting GDP using machine-learning algorithms: A real-time assessment

Can machine-learning algorithms help central banks understand the current state of the economy? Our results say yes! We contribute to the emerging literature on forecasting macroeconomic variables using machine-learning algorithms by testing the nowcast performance of common algorithms in a full ‘real-time’ setting—that is, with real-time vintages of New Zealand GDP growth (our target variable) and real-time vintages of around 600 predictors. Our results show that machine-learning algorithms are able to significantly improve over a simple autoregressive benchmark and a dynamic factor model. We also show that machine-learning algorithms have the potential to add value to, and in one case improve on, the official forecasts of the Reserve Bank of New Zealand.     

Regional differences in residential demand for very high bandwidth broadband internet in 2025

The future demand for data and the role of gigabit networks are central issues in the context of Next Generation Access (NGA) network roll-out. Based on a generic model, which allows to predict unconstrained future broadband demand in different regions and countries, the authors compare the results for Germany, the UK and the Flemish region, and discuss reasons for the different outcomes. The generic market potential model thereby allows to project the future demand for bandwidth from residential customers on the basis of applications and their bandwidth needs, user profiles and population structure on a household level. Despite a general trend towards an increasing need for broadband, there are clear differences. On the one hand, these point to the relevance of socio-demographic factors for broadband adoption. On the other hand, the relatively high proportion of refusals shows that there is still a need for further educational work on the part of public authorities and providers. Finally, it has to be stated, that our forecast relies on the assumption that connectivity and thus that the availability of area-wide gigabit capable broadband access does not represent a bottleneck.     

Do survey joiners and leavers differ from regular participants? The US SPF GDP growth and inflation forecasts

If ‘learning by doing’ is important for macro-forecasting, newcomers might be different from regular, established participants. Stayers may also differ from the soon-to-leave. We test these conjectures for macro-forecasters’ point predictions of output growth and inflation, and for their histogram forecasts. Histogram forecasts of inflation by both joiners and leavers are found to be less accurate, especially if we suppose that joiners take time to learn. For GDP growth, there is no evidence of differences between the groups in terms of histogram forecast accuracy, although GDP point forecasts by leavers are less accurate. These findings are predicated on forecasters being homogeneous within groups. Allowing for individual fixed effects suggests fewer differences, including leavers’ inflation histogram forecasts being no less accurate.     

Assessing the uncertainty in central banks’ inflation outlooks

Recent research has found that macroeconomic survey forecasts of uncertainty exhibit several deficiencies, such as horizon-dependent biases and lower levels of accuracy than simple unconditional uncertainty forecasts. We examine the inflation uncertainty forecasts from the Bank of England, the Banco Central do Brasil, the Magyar Nemzeti Bank and the Sveriges Riksbank to assess whether central banks’ uncertainty forecasts might be subject to similar problems. We find that, while most central banks’ uncertainty forecasts also tend to be underconfident at short horizons and overconfident at longer horizons, they are mostly not significantly biased. Moreover, they tend to be at least as precise as unconditional uncertainty forecasts from two different approaches.     

Medium term growth forecasts: Experts vs. simple models

We compare the medium-term GDP growth forecasts generated by experts to those generated by simple models. This study analyzes a large set of forecasts that covers 48 countries from 1997 to 2016. Out-of-sample exercises indicate that no noticeable difference in performance is observed for advanced economies. In contrast, in the case of emerging economies, model forecasts perform better than expert forecasts. In addition, similar patterns are found for a collection of forecasts from a different set of experts, which suggests that the reported regularity is prevalent. Further analyses suggest that the documented difference in performance can be explained by an optimism bias, excessive reactions to innovations in growth trajectories, and insufficient responses to the information contained in the current account balance.     

基于关键性风险因素的中国金融状况指标体系构建研究

文章从系统性风险冲击来源的角度拓展并构建了包含关键性风险因素的FCI，并以FCI作为同步指标构建了金融景气指标体系，包括金融一致指数、金融先行指数以及金融滞后指数，在此基础上对我国金融状况进行了预测分析。结果显示：关键性风险因素中，房价波动风险与银行业不良贷款风险惯性特征明显，但也受其他因素的冲击影响，需对其进行风险监控，去产能风险则受其他因素的影响显著，需辅以经济政策与调控措施来保证产能过剩行业的稳定；2018年中国经济增长面临较大不确定性，尽管中国金融状况长期向好的趋势性特征没有改变，但当前以及未来一段时间内中国均以较高的转移概率处于风险积聚区制，且存在着影响中国金融状况稳定的其他因素，应更多关注系统性风险的防控。