International Journal of Forecasting最新文献

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2026-01-01 Epub Date: 2025-10-09 DOI: 10.1016/j.ijforecast.2025.05.003

Laura Coroneo

引用次数: 0

All forecasters are not the same: Systematic patterns in predictive performance 所有的预测者都不一样：预测表现的系统模式

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2026-01-01 Epub Date: 2025-04-24 DOI: 10.1016/j.ijforecast.2025.02.008

Robert W. Rich , Joseph Tracy

引用次数: 0

Beyond the numbers: The role of people and processes in central bank forecasting 数字之外：人和流程在央行预测中的作用

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2026-01-01 Epub Date: 2025-11-19 DOI: 10.1016/j.ijforecast.2025.11.001

Nikolaos Kourentzes , Robert Fildes

{"title":"Beyond the numbers: The role of people and processes in central bank forecasting","authors":"Nikolaos Kourentzes , Robert Fildes","doi":"10.1016/j.ijforecast.2025.11.001","DOIUrl":"10.1016/j.ijforecast.2025.11.001","url":null,"abstract":"<div><div>We complement the previous discussions of Bernanke’s review of the Bank of England’s forecasting activities and highlight directions for future research that are relevant to central banks and the wider forecasting community. Decisions in central banks, such as monetary policy ones, are hardly algorithmic and are often influenced by policy and current soft contextual information, introducing challenges into evaluating and specifying forecasts. The use of alternatives to standard econometric models is highlighted in the Bernanke report and other commentaries in this series. These methodological alternatives require both more research, to be validly applied and evaluated, and a cultural shift for those with forecasting responsibilities in central banks. Critically, uncertainty estimates in central bank forecasts are hardly purely model-based. How this is done and how to best communicate it to stakeholders and counterparties are fertile areas for research with potentially important implications for market participants. Finally, while academic research often focuses on large, well-funded central banks, there is a significant opportunity to help smaller, less-resourced institutions.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"42 1","pages":"Pages 40-43"},"PeriodicalIF":7.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal text-based time-series indices 最佳的基于文本的时间序列索引

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2026-01-01 Epub Date: 2025-08-09 DOI: 10.1016/j.ijforecast.2025.07.003

David Ardia , Keven Bluteau

引用次数: 0

Reactions to the Bernanke Review from Bank of England watchers 英国央行观察人士对伯南克评论的反应

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2026-01-01 Epub Date: 2025-05-19 DOI: 10.1016/j.ijforecast.2025.03.006

David Aikman , Richard Barwell

引用次数: 0

Could the Bank of England have avoided mis-forecasting UK inflation during 2021–24? 英国央行（Bank of England）本可以避免错误预测2021 - 2024年英国通胀吗？

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2026-01-01 Epub Date: 2025-10-08 DOI: 10.1016/j.ijforecast.2025.07.001

Jennifer L. Castle , Jurgen A. Doornik , David F. Hendry

引用次数: 0

Deep switching state space model for nonlinear time series forecasting with regime switching 带状态切换的非线性时间序列预测的深度切换状态空间模型

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2026-01-01 Epub Date: 2025-06-30 DOI: 10.1016/j.ijforecast.2025.05.001

Xiuqin Xu, Hanqiu Peng, Ying Chen

{"title":"Deep switching state space model for nonlinear time series forecasting with regime switching","authors":"Xiuqin Xu, Hanqiu Peng, Ying Chen","doi":"10.1016/j.ijforecast.2025.05.001","DOIUrl":"10.1016/j.ijforecast.2025.05.001","url":null,"abstract":"<div><div>Modern time series data often display complex nonlinear dependencies along with irregular regime-switching behaviors. These features present technical challenges in modeling, inference, and providing insightful understanding of the underlying stochastic phenomena. To tackle these challenges, we introduce the novel Deep Switching State Space Model (DS<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>M). In DS<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span><span>M, the architecture employs discrete latent variables to represent regimes and continuous latent variables to account for random driving factors. By melding a Recurrent Neural Network<span> (RNN) with a nonlinear Switching State Space Model (SSSM), we manage to capture the nonlinear dependencies and irregular regime-switching behaviors, governed by a Markov chain<span> and parameterized using multilayer perceptrons. We validate the DS</span></span></span><span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>M through short- and long-term forecasting on a wide array of simulated and real-world datasets, spanning sectors such as healthcare, economics, traffic, meteorology, and energy. Our results reveal that DS<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>M outperforms several state-of-the-art models in terms of forecasting accuracy, while providing meaningful regime identifications.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"42 1","pages":"Pages 85-98"},"PeriodicalIF":7.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Citizen forecasting in a mixed electoral system 混合选举制度下的公民预测

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2026-01-01 Epub Date: 2025-04-30 DOI: 10.1016/j.ijforecast.2025.03.007

Arndt Leininger , Andreas E. Murr , Lukas Stötzer , Mark A. Kayser

引用次数: 0

VAR Model with Sparse Group LASSO for Multi-population Mortality Forecasting 多种群死亡率预测的稀疏群LASSO VAR模型

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2026-01-01 Epub Date: 2025-04-16 DOI: 10.1016/j.ijforecast.2025.03.004

Tim J. Boonen, Yuhuai Chen

引用次数: 0

Anticipating humanitarian emergencies with a high risk of conflict-induced displacement 预见人道主义紧急情况，冲突引发的流离失所风险很高

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2026-01-01 Epub Date: 2025-05-22 DOI: 10.1016/j.ijforecast.2025.04.006

Nicolas Rost , Michele Ronco

{"title":"Anticipating humanitarian emergencies with a high risk of conflict-induced displacement","authors":"Nicolas Rost , Michele Ronco","doi":"10.1016/j.ijforecast.2025.04.006","DOIUrl":"10.1016/j.ijforecast.2025.04.006","url":null,"abstract":"<div><div>This exploratory study assesses the risk of future onset of large-scale, conflict-related internal displacement in countries facing humanitarian emergencies. We train a variety of machine learning models on near-real-time data, which we compare against a simple baseline model, to assess the risk, one and three months into the future, of whether at least 1,000 people per month will flee their homes due to conflict. Measures of past displacement, conflict, risk of humanitarian crises, humanitarian access, the severity of humanitarian crises, and free elections improve forecasting performance. Limitations include the fact that displacement onsets are rare and hard to predict, and limited data availability and quality. Still, the best random forest model flagged 24 of 26 cases of displacement onset three months into the future and identified a high-risk group of country-months with a 33 times higher probability of displacement onset than a low-risk group. Providing such monthly forecasts to humanitarian practitioners could help them prepare better for new displacement or even mitigate the human suffering caused by conflict.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"42 1","pages":"Pages 138-157"},"PeriodicalIF":7.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0