International Journal of Forecasting最新文献_第4页

A modified VAR-deGARCH model for asynchronous multivariate financial time series via variational Bayesian inference 通过变异贝叶斯推理建立异步多变量金融时间序列的修正 VAR-deGARCH 模型

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-06-21 DOI: 10.1016/j.ijforecast.2024.06.002

Wei-Ting Lai , Ray-Bing Chen , Shih-Feng Huang

引用次数: 0

ABC-based forecasting in misspecified state space models 基于ABC的失范状态空间模型预测

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-06-19 DOI: 10.1016/j.ijforecast.2024.05.005

Chaya Weerasinghe, Rubén Loaiza-Maya, Gael M. Martin, David T. Frazier

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引用次数: 0

Predicting the equity premium around the globe: Comprehensive evidence from a large sample 预测全球股票溢价：来自大样本的综合证据

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-06-08 DOI: 10.1016/j.ijforecast.2024.05.002

Fabian Hollstein , Marcel Prokopczuk , Björn Tharann , Chardin Wese Simen

引用次数: 0

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