International Journal of Forecasting最新文献_第6页

Does economic uncertainty predict real activity in real time? 经济的不确定性能实时预测实际活动吗？

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-07-20 DOI: 10.1016/j.ijforecast.2024.06.008

Bart Keijsers , Dick van Dijk

{"title":"Does economic uncertainty predict real activity in real time?","authors":"Bart Keijsers , Dick van Dijk","doi":"10.1016/j.ijforecast.2024.06.008","DOIUrl":"10.1016/j.ijforecast.2024.06.008","url":null,"abstract":"<div><div>We assess the predictive ability of 15 economic uncertainty measures in a real-time out-of-sample forecasting exercise for The Conference Board’s coincident economic index and its components (industrial production, employment, personal income, and manufacturing and trade sales). The results show that the measures hold (real-time) predictive power for quantiles in the left tail. Because uncertainty measures are all proxies of an unobserved entity, we combine their information using principal component analysis. A large fraction of the variance of the uncertainty measures can be explained by two factors: a general economic uncertainty factor with a slight tilt toward financial conditions, and a consumer/media confidence index which remains elevated after recessions. Using a predictive regression model with the factors from the set of uncertainty measures yields more consistent gains compared to a model with an individual uncertainty measure. Further, although accurate forecasts are obtained using the National Financial Conditions Index (NFCI), the uncertainty factor models are better when forecasting employment, and in general, the uncertainty factors have predictive content that is complementary to the NFCI.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 748-762"},"PeriodicalIF":6.9,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579230","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

Adaptively aggregated forecast for exponential family panel model 指数族面板模型的自适应汇总预测

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-07-19 DOI: 10.1016/j.ijforecast.2024.06.005

Dalei Yu , Nian-Sheng Tang , Yang Shi

{"title":"Adaptively aggregated forecast for exponential family panel model","authors":"Dalei Yu , Nian-Sheng Tang , Yang Shi","doi":"10.1016/j.ijforecast.2024.06.005","DOIUrl":"10.1016/j.ijforecast.2024.06.005","url":null,"abstract":"<div><div>Aggregation strategies play an important role akin to that of model selection and have been extensively studied in different statistical models to improve forecasting accuracy. However, traditional aggregated forecast strategies for panel data are mainly developed under the assumption that response variables are continuously distributed (or normally distributed). Replacing this assumption by a more general family of distributions, i.e., exponential family distributions, this paper proposes a computationally efficient way to construct the cumulative risk function and to explicitly accommodate the correlation structure of within-subject observations, develops two novel adaptively aggregated forecasting strategies via exponential reweighting and quadratic reweighting, and rigorously establishes the corresponding tight oracle inequalities. The proposed exponential reweighting-based strategy enjoys promising Kullback–Leibler risk-bound adaptation. Moreover, under the quadratic risk, a promising adaptation property can be achieved by the quadratic reweighting-based strategy. The risk-bound properties of the two proposed procedures in the presence of pre-screening are established under mild conditions. The calibration properties of the proposed methods are also analyzed. Simulation studies, together with an example in analyzing television viewers’ binary decision sequence of watching drama episodes, verify the superiority of our methods over existing model selection and aggregation methods.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 733-747"},"PeriodicalIF":6.9,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848769","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

Predicting and optimizing the fair allocation of donations in hunger relief supply chains 预测和优化饥饿救济供应链中捐款的公平分配

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-07-10 DOI: 10.1016/j.ijforecast.2024.06.004

Nowshin Sharmile , Isaac A. Nuamah , Lauren Davis , Funda Samanlioglu , Steven Jiang , Carter Crain

引用次数: 0

Emotions and the status quo: The anti-incumbency bias in political prediction markets 情绪与现状：政治预测市场中的反在位偏见

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-06-28 DOI: 10.1016/j.ijforecast.2024.06.003

Vahid Karimi Motahhar , Thomas S. Gruca , Mohammad Hosein Tavakoli

{"title":"Emotions and the status quo: The anti-incumbency bias in political prediction markets","authors":"Vahid Karimi Motahhar , Thomas S. Gruca , Mohammad Hosein Tavakoli","doi":"10.1016/j.ijforecast.2024.06.003","DOIUrl":"10.1016/j.ijforecast.2024.06.003","url":null,"abstract":"<div><div>Emotions are often associated with politics, with new research confirming this connection. There is a link between negative emotions and political actions that oppose an incumbent candidate or party. We examine whether this “anti-incumbency” bias extends to political prediction markets, where such emotions can conflict with economic rationality. We analyze unique data from <em>Media Predict</em>, a commercial prediction market. Before a trade is executed, participants are asked to write a justification for their actions. Using text analysis, we measure the emotional sentiment of the justifications of traders buying contracts predicting a change in the incumbent candidate or party. Consistent with anti-incumbency bias, the justifications of buyers of a challenger contract had significantly more negative emotional sentiment scores. We document this finding in prediction markets associated with the 2012 US Presidential Election and the 2015 UK General Election. We conclude that, despite incentives to the contrary, traders’ actions in political stock markets are associated with strong emotions tied to incumbency status.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 571-579"},"PeriodicalIF":6.9,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579119","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

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

{"title":"ABC-based forecasting in misspecified state space models","authors":"Chaya Weerasinghe, Rubén Loaiza-Maya, Gael M. Martin, David T. Frazier","doi":"10.1016/j.ijforecast.2024.05.005","DOIUrl":"10.1016/j.ijforecast.2024.05.005","url":null,"abstract":"<div><div>Approximate Bayesian Computation (ABC) has gained popularity as a method for conducting inference and forecasting in complex models, most notably those which are intractable in some sense. In this paper, we use ABC to produce probabilistic forecasts in state space models (SSMs). Whilst ABC-based forecasting in correctly-specified SSMs has been studied, the misspecified case has not been investigated. It is this case that we emphasize. We invoke recent principles of ‘focused’ Bayesian prediction, whereby Bayesian updates are driven by a scoring rule that rewards predictive accuracy; the aim being to produce predictives that perform well in that rule, despite misspecification. Two methods are investigated for producing the focused predictions. In a simulation setting, ‘coherent’ predictions are in evidence for both methods. That is, the predictive constructed using a particular scoring rule often predicts best according to that rule. Importantly, both focused methods typically produce more accurate forecasts than an exact but misspecified predictive, in particular when the degree of misspecification is marked. An empirical application to a truly intractable SSM completes the paper.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 270-289"},"PeriodicalIF":6.9,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705211","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

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

{"title":"Multi-view locally weighted regression for loss given default forecasting","authors":"Hui Cheng, Cuiqing Jiang, Zhao Wang, Xiaoya Ni","doi":"10.1016/j.ijforecast.2024.05.006","DOIUrl":"10.1016/j.ijforecast.2024.05.006","url":null,"abstract":"<div><div>Accurately forecasting loss given default (LGD) poses challenges, due to its highly skewed distributions and complex nonlinear dependencies with predictors. To this end, we propose a multi-view locally weighted regression (MVLWR) method for LGD forecasting. To address the complexity of LGD distributions, we build a specific ensemble LGD forecasting model tailored for each new sample, providing flexibility and relaxing reliance on distribution assumptions. To address complex relationships, we combine multi-view learning and ensemble learning<span> for LGD modeling. Specifically, we divide original features into multiple complementary groups, build a view-specific locally weighted model for each group, and aggregate the outputs from all view-specific models. An empirical evaluation using a real-world dataset shows that the proposed method outperforms all the benchmarked methods in terms of both out-of-sample and out-of-time performance in LGD forecasting. We also provide valuable insights and practical implications for stakeholders, particularly financial institutions, to enhance their LGD forecasting capabilities.</span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 290-306"},"PeriodicalIF":6.9,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141413761","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

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