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International Journal of Forecasting最新文献

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Forecasting house price growth rates with factor models and spatio-temporal clustering 利用要素模型和时空聚类预测房价增长率
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-10-10 DOI: 10.1016/j.ijforecast.2024.09.003
Raffaele Mattera , Philip Hans Franses
{"title":"Forecasting house price growth rates with factor models and spatio-temporal clustering","authors":"Raffaele Mattera ,&nbsp;Philip Hans Franses","doi":"10.1016/j.ijforecast.2024.09.003","DOIUrl":"10.1016/j.ijforecast.2024.09.003","url":null,"abstract":"<div><div>This paper proposes to use factor models with cluster structure to forecast growth rates of house prices in the US. We assume the presence of global and cluster-specific factors and that the clustering structure is unknown. We adopt a computational procedure that automatically estimates the number of global factors, the clustering structure and the number of clustered factors. The procedure enhances spatial clustering so that the nature of clustered factors reflects the similarity of the time series in the time domain and their spatial proximity. Considering house prices in 1975–2023, we highlight the existence of four main clusters in the US. Moreover, we show that forecasting approaches incorporating global and cluster-specific factors provide more accurate forecasts than models using only global factors and models without factors.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 398-417"},"PeriodicalIF":6.9,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705207","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
Humans vs. large language models: Judgmental forecasting in an era of advanced AI
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-10-08 DOI: 10.1016/j.ijforecast.2024.07.003
Mahdi Abolghasemi , Odkhishig Ganbold , Kristian Rotaru
{"title":"Humans vs. large language models: Judgmental forecasting in an era of advanced AI","authors":"Mahdi Abolghasemi ,&nbsp;Odkhishig Ganbold ,&nbsp;Kristian Rotaru","doi":"10.1016/j.ijforecast.2024.07.003","DOIUrl":"10.1016/j.ijforecast.2024.07.003","url":null,"abstract":"<div><div>This study investigates the forecasting accuracy of human experts versus large language models (LLMs) in the retail sector, particularly during standard and promotional sales periods. Utilizing a controlled experimental setup with 123 human forecasters and five LLMs—namely, ChatGPT-4, ChatGPT3.5, Bard, Bing, and Llama2—we evaluated forecasting precision through the absolute percentage error. Our analysis centered on the effect of the following factors on forecasters’ performance: the supporting statistical model (baseline and advanced), whether the product was on promotion, and the nature of external impact. The findings indicate that LLMs do not consistently outperform humans in forecasting accuracy and that advanced statistical forecasting models do not uniformly enhance the performance of either human forecasters or LLMs. Both human and LLM forecasters exhibited increased forecasting errors, particularly during promotional periods. Our findings call for careful consideration when integrating LLMs into practical forecasting processes.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 631-648"},"PeriodicalIF":6.9,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579225","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
Forecasting realized volatility with spillover effects: Perspectives from graph neural networks 预测具有溢出效应的已实现波动率：图神经网络的视角
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-10-07 DOI: 10.1016/j.ijforecast.2024.09.002
Chao Zhang , Xingyue Pu , Mihai Cucuringu , Xiaowen Dong
{"title":"Forecasting realized volatility with spillover effects: Perspectives from graph neural networks","authors":"Chao Zhang ,&nbsp;Xingyue Pu ,&nbsp;Mihai Cucuringu ,&nbsp;Xiaowen Dong","doi":"10.1016/j.ijforecast.2024.09.002","DOIUrl":"10.1016/j.ijforecast.2024.09.002","url":null,"abstract":"<div><div>We present a novel nonparametric methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks. The proposed model offers the benefits of incorporating spillover effects from multi-hop neighbors, capturing nonlinear relationships, and flexible training with different loss functions. The empirical findings suggest that incorporating spillover effects from multi-hop neighbors alone does not yield a clear advantage in terms of predictive accuracy. Furthermore, modeling nonlinear spillover effects enhances the forecasting accuracy of realized volatilities, particularly for short-term horizons of up to one week. More importantly, our results consistently indicate that training with the quasi-likelihood loss leads to substantial improvements in model performance compared to the commonly used mean squared error, primarily due to its superior handling of heteroskedasticity. A comprehensive series of empirical evaluations in alternative settings confirm the robustness of our results.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 377-397"},"PeriodicalIF":6.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705206","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
Sparse time-varying parameter VECMs with an application to modeling electricity prices 稀疏时变参数 VECMs 在电价建模中的应用
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-09-26 DOI: 10.1016/j.ijforecast.2024.09.001
Niko Hauzenberger , Michael Pfarrhofer , Luca Rossini
{"title":"Sparse time-varying parameter VECMs with an application to modeling electricity prices","authors":"Niko Hauzenberger ,&nbsp;Michael Pfarrhofer ,&nbsp;Luca Rossini","doi":"10.1016/j.ijforecast.2024.09.001","DOIUrl":"10.1016/j.ijforecast.2024.09.001","url":null,"abstract":"<div><div>In this paper we propose a time-varying parameter (TVP) vector error correction model (VECM) with heteroskedastic disturbances. We propose tools to carry out dynamic model specification in an automatic fashion. This involves using global–local priors and postprocessing the parameters to achieve truly sparse solutions. Depending on the respective set of coefficients, we achieve this by minimizing auxiliary loss functions. Our two-step approach limits overfitting and reduces parameter estimation uncertainty. We apply this framework to modeling European electricity prices. When considering daily electricity prices for different markets jointly, our model highlights the importance of explicitly addressing cointegration and nonlinearities. In a forecasting exercise focusing on hourly prices for Germany, our approach yields competitive metrics of predictive accuracy.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 361-376"},"PeriodicalIF":6.9,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705205","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
Guest editorial: Forecasting for social good 特邀社论：社会公益预测
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-09-20 DOI: 10.1016/j.ijforecast.2024.08.007
Bahman Rostami-Tabar, Pierre Pinson, Michael D. Porter
{"title":"Guest editorial: Forecasting for social good","authors":"Bahman Rostami-Tabar,&nbsp;Pierre Pinson,&nbsp;Michael D. Porter","doi":"10.1016/j.ijforecast.2024.08.007","DOIUrl":"10.1016/j.ijforecast.2024.08.007","url":null,"abstract":"","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 1-2"},"PeriodicalIF":6.9,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704580","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
Cognitive reflection, arithmetic ability and financial literacy independently predict both inflation expectations and forecast accuracy
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-09-20 DOI: 10.1016/j.ijforecast.2024.06.011
David A. Comerford
{"title":"Cognitive reflection, arithmetic ability and financial literacy independently predict both inflation expectations and forecast accuracy","authors":"David A. Comerford","doi":"10.1016/j.ijforecast.2024.06.011","DOIUrl":"10.1016/j.ijforecast.2024.06.011","url":null,"abstract":"<div><div>Cognitive reflection is defined as the tendency to detect and check intuitive errors and has been found to predict forecast accuracy in a range of domains. The current research demonstrates in a purpose-designed survey that a question in the Survey of Consumer Expectations serves as a test of cognitive reflection. Using this measure, I demonstrate for the first time in a time-series of inflation expectations that cognitive reflection is associated with greater forecast accuracy. I then apply this insight to interrogate the spike in inflation expectations that occurred over the year 2021. The data rule out that the spike was driven by respondents low in cognitive reflection, who are most vulnerable to overreacting to recent news. These results are insightful for the use of survey data not only in forecasting inflation but in forecasting more generally.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 517-531"},"PeriodicalIF":6.9,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579419","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
Improving out-of-population prediction: The complementary effects of model assistance and judgmental bootstrapping
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-09-17 DOI: 10.1016/j.ijforecast.2024.07.002
Mathew D. Hardy , Sam Zhang , Jessica Hullman , Jake M. Hofman , Daniel G. Goldstein
{"title":"Improving out-of-population prediction: The complementary effects of model assistance and judgmental bootstrapping","authors":"Mathew D. Hardy ,&nbsp;Sam Zhang ,&nbsp;Jessica Hullman ,&nbsp;Jake M. Hofman ,&nbsp;Daniel G. Goldstein","doi":"10.1016/j.ijforecast.2024.07.002","DOIUrl":"10.1016/j.ijforecast.2024.07.002","url":null,"abstract":"<div><div>We propose and test a method for out-of-population prediction termed model-assisted judgmental bootstrapping, which leverages a predictive model from one domain combined with expert judgment to generate training data and subsequently a predictive model for a new domain. In a preregistered experiment (<span><math><mi>N</mi></math></span>=1440), we assessed the predictive accuracy of this method in increasingly challenging environments. We also analyzed the individual contributions of two techniques that underlie the method: model-assisted estimation and judgmental bootstrapping. Our findings revealed that both techniques significantly improved predictive accuracy. Furthermore, their impacts were complementary: model-assisted estimation provided the largest accuracy gains in the least demanding environment, while judgmental bootstrapping did so in the most challenging environment. Our results suggest that model-assisted judgmental bootstrapping is a promising technique for creating predictive models in domains in which outcome data are not available.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 689-701"},"PeriodicalIF":6.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579227","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
Factors affecting preferences between judgmental and algorithmic forecasts: Feedback, guidance and labeling effects
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-09-16 DOI: 10.1016/j.ijforecast.2024.08.002
Nigel Harvey , Shari De Baets
{"title":"Factors affecting preferences between judgmental and algorithmic forecasts: Feedback, guidance and labeling effects","authors":"Nigel Harvey ,&nbsp;Shari De Baets","doi":"10.1016/j.ijforecast.2024.08.002","DOIUrl":"10.1016/j.ijforecast.2024.08.002","url":null,"abstract":"<div><div>Previous research has shown that people prefer algorithmic to judgmental forecasts in the absence of outcome feedback but judgmental to algorithmic forecasts when feedback is provided. However, all this work has used cue-based forecasting tasks. The opposite pattern of results has been reported for time series forecasting tasks. This reversal could have arisen because cue-based forecasting studies have used preference paradigms whereas the time series forecasting studies have employed advice-taking paradigms. In a first experiment, we show that when a preference paradigm is used in time series forecasting, the difference in the conclusions about the effects of feedback in the two types of forecasting disappears. In a second experiment, we show that provision of guidance showing accuracy of algorithmic and judgmental forecasts can eliminate effects of feedback. Two further experiments reveal how choices between algorithmic and judgmental forecasts are influenced by the way in which those forecasts are labeled.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 532-553"},"PeriodicalIF":6.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579418","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
Guiding supervisors in artificial intelligence-enabled forecasting: Understanding the impacts of salience and detail on decision-making
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-09-02 DOI: 10.1016/j.ijforecast.2024.08.001
Naghmeh Khosrowabadi , Kai Hoberg , Yun Shin Lee
{"title":"Guiding supervisors in artificial intelligence-enabled forecasting: Understanding the impacts of salience and detail on decision-making","authors":"Naghmeh Khosrowabadi ,&nbsp;Kai Hoberg ,&nbsp;Yun Shin Lee","doi":"10.1016/j.ijforecast.2024.08.001","DOIUrl":"10.1016/j.ijforecast.2024.08.001","url":null,"abstract":"<div><div>In many real-world situations, multiple humans are involved in decision-making when interacting with machine recommendations. We investigated a setting where an artificial intelligence system creates demand forecasts that a human planner can either accept or revise, and a supervisor then makes the final decision about which forecast to select. We designed and conducted two experimental studies to understand decision-making by a supervisor. First, we provided the improvement probabilities of adjustments at an aggregated level and found evidence for overoptimism bias and mean anchoring. Second, we provided decomposed guidance based on two adjustment attributes, direction and magnitude, to investigate the role of salience based on the distance between the improvement probabilities and level of detail in guidance effectiveness. We found no significant difference in using less and more salient guidance provided that the detail level was fixed. However, revealing more details when the guidance was more salient increased the use of guidance.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 716-732"},"PeriodicalIF":6.9,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579229","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
On memory-augmented gated recurrent unit network 关于记忆增强型门控递归单元网络
IF 6.9 2区 经济学
International Journal of Forecasting Pub Date : 2024-08-31 DOI: 10.1016/j.ijforecast.2024.07.008
Maolin Yang , Muyi Li , Guodong Li
{"title":"On memory-augmented gated recurrent unit network","authors":"Maolin Yang ,&nbsp;Muyi Li ,&nbsp;Guodong Li","doi":"10.1016/j.ijforecast.2024.07.008","DOIUrl":"10.1016/j.ijforecast.2024.07.008","url":null,"abstract":"<div><div>This paper addresses the challenge of forecasting multivariate long-memory time series. While statistical models such as the autoregressive fractionally integrated moving average (ARFIMA) and hyperbolic generalized autoregressive conditional heteroscedasticity (HYGARCH) can capture long-memory effects in time series data, they are often limited by dimensionality and parametric specification. Alternatively, recurrent neural networks (RNNs) are popular tools for approximating complex structures in sequential data. However, the lack of long-memory effect of these networks has been justified from a statistical perspective. In this paper, we propose a new network process called the memory-augmented gated recurrent unit (MGRU), which incorporates a fractionally integrated filter into the original GRU structure. We investigate the long-memory effect of the MGRU process, and demonstrate its effectiveness at capturing long-range dependence in real applications. Our findings illustrate that the proposed MGRU network outperforms existing models, indicating its potential as a promising tool for long-memory time series forecasting.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 844-858"},"PeriodicalIF":6.9,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190563","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
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