International Journal of Forecasting最新文献_第9页

Multi-view locally weighted regression for loss given default forecasting 用于给定违约损失预测的多视角局部加权回归

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-06-08 DOI: 10.1016/j.ijforecast.2024.05.006

Hui Cheng, Cuiqing Jiang, Zhao Wang, Xiaoya Ni

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引用次数: 0

Forecasting macroeconomic tail risk in real time: Do textual data add value? 实时预测宏观经济尾部风险：文本数据会带来价值吗？

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-06-06 DOI: 10.1016/j.ijforecast.2024.05.007

Philipp Adämmer , Jan Prüser , Rainer A. Schüssler

引用次数: 0

Cross-temporal forecast reconciliation at digital platforms with machine learning 利用机器学习实现数字平台的跨时空预测调节

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-06-02 DOI: 10.1016/j.ijforecast.2024.05.008

Jeroen Rombouts , Marie Ternes , Ines Wilms

引用次数: 0

Asymmetric uncertainty: Nowcasting using skewness in real-time data 非对称不确定性：利用实时数据的偏度进行预测

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-05-29 DOI: 10.1016/j.ijforecast.2024.05.003

Paul Labonne

引用次数: 0

Dynamic time series modelling and forecasting of COVID-19 in Norway 挪威 COVID-19 的动态时间序列建模和预测

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-05-25 DOI: 10.1016/j.ijforecast.2024.05.004

Gunnar Bårdsen , Ragnar Nymoen

引用次数: 0

Boosting domain-specific models with shrinkage: An application in mortality forecasting 利用收缩技术提升特定领域模型：死亡率预测中的应用

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-05-20 DOI: 10.1016/j.ijforecast.2024.05.001

Li Li , Han Li , Anastasios Panagiotelis

引用次数: 0

Efficiency of poll-based multi-period forecasting systems for German state elections 基于民调的德国州选举多期预测系统的效率

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-05-18 DOI: 10.1016/j.ijforecast.2024.04.008

Markus Fritsch , Harry Haupt , Joachim Schnurbus

{"title":"Efficiency of poll-based multi-period forecasting systems for German state elections","authors":"Markus Fritsch , Harry Haupt , Joachim Schnurbus","doi":"10.1016/j.ijforecast.2024.04.008","DOIUrl":"10.1016/j.ijforecast.2024.04.008","url":null,"abstract":"<div><div>Election polls are frequently employed to reflect voter sentiment with respect to a particular election (or fixed-event). Despite their widespread use as forecasts and inputs for predictive algorithms, there is substantial uncertainty regarding their efficiency. This uncertainty is amplified by judgment in the form of pollsters applying unpublished weighting schemes to ensure the representativeness of the sampled voters for the underlying population. Efficient forecasting systems incorporate past information instantly, which renders a given fixed-event unpredictable based on past information. This results in all sequential adjustments of the fixed-event forecasts across adjacent time periods (or forecast revisions) being martingale differences. This paper illustrates the theoretical conditions related to weak efficiency of fixed-event forecasting systems based on traditional least squares loss and asymmetrically weighted least absolute deviations (or quantile) loss. Weak efficiency of poll-based multi-period forecasting systems for all German federal state elections since the year 2000 is investigated. The inefficiency of almost all considered forecasting systems is documented and alternative explanations for the findings are discussed.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 670-688"},"PeriodicalIF":6.9,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141135428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The time-varying Multivariate Autoregressive Index model 时变多元自回归指数模型

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-05-15 DOI: 10.1016/j.ijforecast.2024.04.007

Gianluca Cubadda , Stefano Grassi , Barbara Guardabascio

引用次数: 0

Light-touch forecasting: A novel method to combine human judgment with statistical algorithms 轻触式预测：将人类判断与统计算法相结合的新方法

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-05-13 DOI: 10.1016/j.ijforecast.2024.04.003

B.B.J.P.J. van der Staak, R.J.I. Basten, P.P.F.M. van de Calseyde, E. Demerouti, A.G. de Kok

{"title":"Light-touch forecasting: A novel method to combine human judgment with statistical algorithms","authors":"B.B.J.P.J. van der Staak, R.J.I. Basten, P.P.F.M. van de Calseyde, E. Demerouti, A.G. de Kok","doi":"10.1016/j.ijforecast.2024.04.003","DOIUrl":"10.1016/j.ijforecast.2024.04.003","url":null,"abstract":"<div><div>Forecast adjustments are an indispensable component of the forecasting process, but what is the most effective and efficient method to make these adjustments? Understanding how to effectively blend human forecast adjustments with statistical methods is of great importance as, even with the increased possibilities of AI, we can still not include all information available to a human planner. We address this issue by developing a method that builds on literature showing that some adjustments are consistently (in)accurate. More specifically, in two extensive case studies containing more than 3.5 million forecasting decisions, we confirm that planners are accurate in adjusting a statistical forecast in the right direction and determining the magnitude of downward adjustments, and they are inaccurate in determining the magnitude of upward adjustments. Leveraging these results, we introduce a novel method called <em>light-touch forecasting,</em> which attains performance levels similar to those of more traditional forecasting methods while minimizing the involvement of human planners. Furthermore, an online experiment shows an efficiency gain of 38% in terms of time spent on planning compared to traditional judgmental forecasting. We thus optimize the forecasting process by using the strengths of planners while avoiding their weaknesses.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 440-451"},"PeriodicalIF":6.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141057009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Judgment in macroeconomic output growth predictions: Efficiency, accuracy and persistence 对宏观经济产出增长预测的判断：效率、准确性和持久性

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-05-13 DOI: 10.1016/j.ijforecast.2024.04.001

Michael Pedersen

引用次数: 0