International Journal of Forecasting最新文献_第4页

An extended logarithmic visualization improves forecasting accuracy for exponentially growing numbers, but residual difficulties remain 扩展的对数可视化提高了指数增长数字的预测精度，但仍然存在一些困难

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-10-24 DOI: 10.1016/j.ijforecast.2024.09.006

Ben H. Engler, Florian Hutzler, Stefan Hawelka

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引用次数: 0

Trust the experts? The performance of inflation expectations, 1960–2023 相信专家？通胀预期的表现，1960-2023

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-10-18 DOI: 10.1016/j.ijforecast.2024.06.006

Tyler Goodspeed

{"title":"Trust the experts? The performance of inflation expectations, 1960–2023","authors":"Tyler Goodspeed","doi":"10.1016/j.ijforecast.2024.06.006","DOIUrl":"10.1016/j.ijforecast.2024.06.006","url":null,"abstract":"<div><div>Using the oldest continuous surveys of U.S. inflation expectations, I find that when inflation is low, the average professional forecaster is generally rational (unbiased, with serially uncorrelated errors) and efficient (exploits available information), and inflation moves one-for-one with their expectations, whereas the average consumer is biased, underreacts to inflation, and inflation moves independently of their expectations. In contrast, when inflation is high the average consumer is generally unbiased, rational, and efficient, and inflation moves one-for-one with their expectations, while the opposite is true of professionals. Neither the median consumer nor median professional is fully rational and efficient, with the consumer underreacting when inflation is low and overreacting when inflation is high. However, inflation moves one-for-one with the median consumer forecast when inflation is high. Results are consistent with an inflation process characterized by two regimes—a low-inflation regime in which consumers are inattentive, and a high-inflation regime in which consumers are highly attentive and inflation moves with their expectations.<span><span><sup>1</sup></span></span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 863-876"},"PeriodicalIF":6.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212026","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 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

引用次数: 0

Humans vs. large language models: Judgmental forecasting in an era of advanced AI 人类vs.大型语言模型：先进人工智能时代的判断预测

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

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 , Xingyue Pu , Mihai Cucuringu , 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

引用次数: 0

Forecasting CPI inflation under economic policy and geopolitical uncertainties 预测经济政策和地缘政治不确定性下的CPI通胀

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-09-20 DOI: 10.1016/j.ijforecast.2024.08.005

Shovon Sengupta , Tanujit Chakraborty , Sunny Kumar Singh

{"title":"Forecasting CPI inflation under economic policy and geopolitical uncertainties","authors":"Shovon Sengupta , Tanujit Chakraborty , Sunny Kumar Singh","doi":"10.1016/j.ijforecast.2024.08.005","DOIUrl":"10.1016/j.ijforecast.2024.08.005","url":null,"abstract":"<div><div>Forecasting consumer price index (CPI) inflation is of paramount importance for both academics and policymakers at central banks. This study introduces the filtered ensemble wavelet neural network (FEWNet) to forecast CPI inflation, tested in BRIC countries. FEWNet decomposes inflation data into high- and low-frequency components using wavelet transforms, and incorporates additional economic factors, such as economic policy uncertainty and geopolitical risk, to enhance forecast accuracy. These wavelet-transformed series and filtered exogenous variables are input into downstream autoregressive neural networks, producing the final ensemble forecast. Theoretically, we demonstrate that FEWNet reduces empirical risk compared to fully connected autoregressive neural networks. Empirically, FEWNet outperforms other forecasting methods and effectively estimates prediction uncertainty, due to its ability to capture non-linearities and long-range dependencies through its adaptable architecture. Consequently, FEWNet emerges as a valuable tool for central banks to manage inflation and enhance monetary policy decisions.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 953-981"},"PeriodicalIF":6.9,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211932","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 认知反思、算术能力和金融素养分别独立预测通胀预期和预测准确性

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

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

引用次数: 0

Improving out-of-population prediction: The complementary effects of model assistance and judgmental bootstrapping 改进人口外预测：模型辅助和判断自举的互补效应

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 , Sam Zhang , Jessica Hullman , Jake M. Hofman , 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