International Journal of Forecasting最新文献_第9页

Investigating laypeople’s short- and long-term forecasts of COVID-19 infection cycles 调查非专业人士对 COVID-19 感染周期的短期和长期预测

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2023-12-15 DOI: 10.1016/j.ijforecast.2023.11.008

Moon Su Koo , Yun Shin Lee , Matthias Seifert

{"title":"Investigating laypeople’s short- and long-term forecasts of COVID-19 infection cycles","authors":"Moon Su Koo , Yun Shin Lee , Matthias Seifert","doi":"10.1016/j.ijforecast.2023.11.008","DOIUrl":"10.1016/j.ijforecast.2023.11.008","url":null,"abstract":"<div><div>How do laypeople anticipate the severity of the COVID-19 pandemic in the short and long term? The evolution of COVID-19 infection cases is characterized by wave-shaped cycles, and we examine how individuals make forecasts for this type of time series<span><span>. Over 42 weeks, we ran forecasting experiments and elicited weekly judgments from the general public to analyze their forecasting behavior<span> (Study 1). We find that laypeople often tend to dampen trends when generating judgmental forecasts, but the degree to which this happens depends on the evolution of the cyclic time series data. The observed forecasting behavior reveals evidence of an </span></span>optimism bias in that people do not expect the number of infection cases to grow at the observed rate while believing that infection rates would drop at an even faster rate than they are. Also, our results suggest that laypeople’s forecasting judgments are affected by the magnitude of the present wave relative to the previously observed ones. Further, we provide evidence that laypeople rely on a cognitive heuristic for generating long-term forecasts. People tend to rely on a linear discounting rule in that they lower their long-term forecasts proportionally to the interval of the forecast horizon, i.e., from tomorrow to 6 months and from 6 months to 1 year. We also find that this linear discounting rule can change to an exponential one in reaction to externally generated optimistic information signals such as vaccine approval. Furthermore, we replicated the major findings of Study 1 in a more controlled setting with a hypothetical pandemic scenario and artificially generated time series (Study 2). Overall, the current research contributes to the judgmental forecasting literature and provides practical implications for decision-makers in the pandemic.</span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 452-465"},"PeriodicalIF":6.9,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138682565","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

Nowcasting with panels and alternative data: The OECD weekly tracker 利用面板和替代数据进行即时预测：经合组织每周跟踪

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2023-12-14 DOI: 10.1016/j.ijforecast.2023.11.005

引用次数: 0

Lantz Brett，《使用R的机器学习：预测建模的专家技术》，第3版，Packt出版有限公司，英国伯明翰（2019年），458页。£57.56.ISBN: 9781788295864, 1788295862

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-12-11 DOI: 10.1016/j.ijforecast.2023.11.002

Gnanadarsha Sanjaya Dissanayake

引用次数: 0

Do professional forecasters believe in the Phillips curve? 专业预测人员相信菲利普斯曲线吗？

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-12-08 DOI: 10.1016/j.ijforecast.2023.11.004

Michael P. Clements

引用次数: 0

Reservoir computing for macroeconomic forecasting with mixed-frequency data 利用混合频率数据进行宏观经济预测的储层计算

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-12-07 DOI: 10.1016/j.ijforecast.2023.10.009

Giovanni Ballarin , Petros Dellaportas , Lyudmila Grigoryeva , Marcel Hirt , Sophie van Huellen , Juan-Pablo Ortega

{"title":"Reservoir computing for macroeconomic forecasting with mixed-frequency data","authors":"Giovanni Ballarin , Petros Dellaportas , Lyudmila Grigoryeva , Marcel Hirt , Sophie van Huellen , Juan-Pablo Ortega","doi":"10.1016/j.ijforecast.2023.10.009","DOIUrl":"10.1016/j.ijforecast.2023.10.009","url":null,"abstract":"<div><p>Macroeconomic forecasting has recently started embracing techniques that can deal with large-scale datasets and series with unequal release periods. Mixed-data sampling (MIDAS) and dynamic factor models (DFMs) are the two main state-of-the-art approaches to modeling series with non-homogeneous frequencies. We introduce a new framework, called the multi-frequency echo state network (MFESN), based on a relatively novel machine learning paradigm called reservoir computing. Echo state networks (ESNs) are recurrent neural networks formulated as nonlinear state-space systems with random state coefficients where only the observation map is subject to estimation. MFESNs are considerably more efficient than DFMs and can incorporate many series, as opposed to MIDAS models, which are prone to the curse of dimensionality. All methods are compared in extensive multistep forecasting exercises targeting U.S. GDP growth. We find that our MFESN models achieve superior or comparable performance over MIDAS and DFMs at a much lower computational cost.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 3","pages":"Pages 1206-1237"},"PeriodicalIF":7.9,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023001085/pdfft?md5=02cc0203937b906c9719e0df65a0dafe&pid=1-s2.0-S0169207023001085-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138545466","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 day-ahead electricity prices with spatial dependence 具有空间依赖性的日前电价预测

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-11-29 DOI: 10.1016/j.ijforecast.2023.11.006

Yifan Yang , Ju’e Guo , Yi Li , Jiandong Zhou

{"title":"Forecasting day-ahead electricity prices with spatial dependence","authors":"Yifan Yang , Ju’e Guo , Yi Li , Jiandong Zhou","doi":"10.1016/j.ijforecast.2023.11.006","DOIUrl":"10.1016/j.ijforecast.2023.11.006","url":null,"abstract":"<div><p>Market integration connects multiple autarkic electricity markets and facilitates the flow of power across areas. More often than not, market integration can increase social welfare for the whole power system with a clear spatial dependence structure among area electricity prices, which motivates a new perspective on electricity price forecasting<span>. In this paper, we construct a model to forecast the day-ahead electricity prices of Nord Pool with spatial dependence. First of all, we convert the electricity prices into graph data. Then, we propose an STGNN (Spatial-Temporal Graph Neural Network) model to exploit spatial and temporal features. In particular, the STGNN model can accurately forecast electricity prices for multiple areas, where the adjacency matrix representing the spatial dependence structure is pre-captured by the R-vine (regular vine) copula. Our results show that the spatial dependence structure described by the R-vine copula can perfectly reflect the physical characteristics of the electricity system; moreover, the forecasting performance of the proposed STGNN model is significantly better than the existing models in terms of overall accuracy and hourly accuracy within a day.</span></p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 3","pages":"Pages 1255-1270"},"PeriodicalIF":7.9,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138539106","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

Nowcasting U.S. state-level CO2 emissions and energy consumption 临近预测美国各州的二氧化碳排放量和能源消耗

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2023-11-18 DOI: 10.1016/j.ijforecast.2023.10.002

Jack Fosten , Shaoni Nandi

{"title":"Nowcasting U.S. state-level CO2 emissions and energy consumption","authors":"Jack Fosten , Shaoni Nandi","doi":"10.1016/j.ijforecast.2023.10.002","DOIUrl":"10.1016/j.ijforecast.2023.10.002","url":null,"abstract":"<div><div>This paper proposes panel nowcasting methods to obtain timely predictions of CO<sub>2</sub> emissions and energy consumption growth across all U.S. states. This is crucial, not least because of the increasing role of sub-national carbon abatement policies but also due to the very delayed publication of the data. Since the state-level CO<sub>2</sub> data are constructed from energy consumption data, we propose a new panel bridge equation method. We use a mixed frequency set-up where economic data are first used to predict energy consumption growth. This is then used to predict CO<sub>2</sub> emissions growth while allowing for cross-sectional dependence across states using estimated factors. We evaluate the models’ performance using an out-of-sample forecasting study. We find that nowcasts improve when incorporating timely data like electricity consumption relative to a simple benchmark. These gains are sizeable in many states, even around two years before the data are eventually released. In predicting CO<sub>2</sub> emissions growth, nowcast accuracy gains are also notable well before the data release, especially after the current year’s energy consumption data are used in making the prediction.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 20-30"},"PeriodicalIF":6.9,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138539107","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

Rating players by Laplace’s approximation and dynamic modeling 通过拉普拉斯近似法和动态建模对球员进行评级

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-11-10 DOI: 10.1016/j.ijforecast.2023.10.004

Hsuan-Fu Hua, Ching-Ju Chang, Tse-Ching Lin, Ruby Chiu-Hsing Weng

引用次数: 0

Portfolio selection under non-gaussianity and systemic risk: A machine learning based forecasting approach 非高斯性和系统性风险下的投资组合选择：基于机器学习的预测方法

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-11-10 DOI: 10.1016/j.ijforecast.2023.10.007

Weidong Lin , Abderrahim Taamouti

引用次数: 0

Obituary: J. Scott Armstrong 讣告:j·斯科特·阿姆斯特朗