International Journal of Forecasting最新文献_第9页

Robust regression for electricity demand forecasting against cyberattacks 针对网络攻击的电力需求预测鲁棒回归

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-10-01 DOI: 10.1016/j.ijforecast.2022.10.004

Daniel VandenHeuvel , Jinran Wu , You-Gan Wang

引用次数: 0

Testing big data in a big crisis: Nowcasting under Covid-19 在大危机中测试大数据:新冠肺炎下的临近预测

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-10-01 DOI: 10.1016/j.ijforecast.2022.10.005

Luca Barbaglia, Lorenzo Frattarolo, Luca Onorante, Filippo Maria Pericoli, Marco Ratto, Luca Tiozzo Pezzoli

{"title":"Testing big data in a big crisis: Nowcasting under Covid-19","authors":"Luca Barbaglia, Lorenzo Frattarolo, Luca Onorante, Filippo Maria Pericoli, Marco Ratto, Luca Tiozzo Pezzoli","doi":"10.1016/j.ijforecast.2022.10.005","DOIUrl":"10.1016/j.ijforecast.2022.10.005","url":null,"abstract":"<div><p>During the Covid-19 pandemic, economists have struggled to obtain reliable economic predictions, with standard models becoming outdated and their forecasting performance deteriorating rapidly. This paper presents two novelties that could be adopted by forecasting institutions in unconventional times. The first innovation is the construction of an extensive data set for macroeconomic forecasting in Europe. We collect more than a thousand time series from conventional and unconventional sources, complementing traditional macroeconomic variables with timely big data indicators and assessing their added value at nowcasting. The second novelty consists of a methodology to merge an enormous amount of non-encompassing data with a large battery of classical and more sophisticated forecasting methods in a seamlessly dynamic Bayesian framework. Specifically, we introduce an innovative “selection prior” that is used not as a way to influence model outcomes, but as a selection device among competing models. By applying this methodology to the Covid-19 crisis, we show which variables are good predictors for nowcasting gross domestic product and draw lessons for dealing with possible future crises.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"39 4","pages":"Pages 1548-1563"},"PeriodicalIF":7.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9633630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10439484","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: 11

Tree-based heterogeneous cascade ensemble model for credit scoring 基于树的异构级联集成信用评分模型

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-10-01 DOI: 10.1016/j.ijforecast.2022.07.007

Wanan Liu , Hong Fan , Meng Xia

{"title":"Tree-based heterogeneous cascade ensemble model for credit scoring","authors":"Wanan Liu , Hong Fan , Meng Xia","doi":"10.1016/j.ijforecast.2022.07.007","DOIUrl":"10.1016/j.ijforecast.2022.07.007","url":null,"abstract":"<div><p>Credit scoring is an important tool to guard against commercial risks for banks and lending companies and provides good conditions for the construction of individual personal credit. Ensemble algorithms have shown appealing progress for the improvement of credit scoring. In this study, to meet the challenge of large-scale credit scoring, we propose a heterogeneous deep forest model (Heter-DF), which is established based on considerations ranging from base learner selection, encouragement of the diversity of base learners, and ensemble strategies, for credit scoring. Heter-DF is designed as a scalable cascading framework that can increase its complexity with the scale of the credit dataset. Moreover, each level of Heter-DF is built by multiple heterogeneous tree-based ensembled base learners, avoiding the homogeneous prediction of the ensemble framework. In addition, a weighted voting mechanism is introduced to highlight important information and suppress irrelevant features, making Heter-DF a robust model for credit scoring. Experimental results on four credit scoring datasets and six evaluation metrics show that the cascading framework a good choice for the ensemble of tree-based base learners. A comparison among homogeneous ensembles and heterogeneous ensembles further demonstrates the effectiveness of Heter-DF. Experiments on different training sets indicate that Heter-DF is a scalable framework which not only deals with large-scale credit scoring but also satisfies the condition where small-scale credit scoring is desirable. Finally, based on the good interpretability of a tree-based structure, the global interpretation of Heter-DF is preliminarily explored.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"39 4","pages":"Pages 1593-1614"},"PeriodicalIF":7.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41728816","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: 4

Internal consistency of household inflation expectations: Point forecasts vs. density forecasts 家庭通胀预期的内部一致性:点预测与密度预测

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-10-01 DOI: 10.1016/j.ijforecast.2022.08.008

Yongchen Zhao

引用次数: 2

IMF trade forecasts for crisis countries: Bias, inefficiency, and their origins 国际货币基金组织对危机国家的贸易预测：偏见、低效及其根源

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-10-01 DOI: 10.1016/j.ijforecast.2022.07.006

Theo S. Eicher, Reina Kawai

{"title":"IMF trade forecasts for crisis countries: Bias, inefficiency, and their origins","authors":"Theo S. Eicher, Reina Kawai","doi":"10.1016/j.ijforecast.2022.07.006","DOIUrl":"10.1016/j.ijforecast.2022.07.006","url":null,"abstract":"<div><p><span>External sector surveillance and stabilization are core missions of the International Monetary Fund (IMF). Since 1992, the IMF approved over 600 crisis country loan programs, conditional on reforms and performance targets that are contingent on IMF crisis assessments and recovery forecasts. The literature evaluating IMF crisis forecasts has primarily focused on GDP, inflation, and fiscal budgets, but IMF programs often originate with the </span>balance of payments<span><span> crises. Our evaluation of IMF imports/exports/exchange rates in crisis countries reveals a surprising dichotomy: import forecasts are largely efficient and unbiased, while exports and exchange rate forecasts exhibit substantial biases and inefficiencies. We show forecast errors in the full sample are driven by deeply flawed IMF forecasts for LICs in crisis. Fixed exchange rate<span> LICs (predominantly African franc zone countries) receive systematically inefficient import forecasts. Exchange rate forecasts for LICs with flexible exchange rates are so inefficient that they cannot outperform a naïve random walk, and over 30 percent of the forecasts cannot match the exchange rate’s directional movement during the first year of the recovery. Examining the sources of biases and inefficiencies, we highlight effects of conditionality and geopolitics that were not fully accounted for in IMF forecasts, specifically those relating to arrears (domestic and foreign), fiscal </span></span>finance (balance and credit limits), policy reforms (trade and government), (civil) wars, and elections.</span></p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"39 4","pages":"Pages 1615-1639"},"PeriodicalIF":7.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47437570","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: 1

Do we want coherent hierarchical forecasts, or minimal MAPEs or MAEs? (We won’t get both!) 我们想要连贯的分层预测，还是最小的mape或MAEs?(我们不可能两者都得到!)

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-10-01 DOI: 10.1016/j.ijforecast.2022.11.006

Stephan Kolassa

引用次数: 4

Identifying predictors of analyst rating quality: An ensemble feature selection approach 识别分析师评级质量的预测因素:一种集成特征选择方法

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-10-01 DOI: 10.1016/j.ijforecast.2022.09.003

Shuai Jiang , Yanhong Guo , Wenjun Zhou , Xianneng Li

{"title":"Identifying predictors of analyst rating quality: An ensemble feature selection approach","authors":"Shuai Jiang , Yanhong Guo , Wenjun Zhou , Xianneng Li","doi":"10.1016/j.ijforecast.2022.09.003","DOIUrl":"10.1016/j.ijforecast.2022.09.003","url":null,"abstract":"<div><p>Forecasting the analyst rating quality (ARQ), defined as whether a stock rating provided by an analyst can correctly foretell the stock movement, is crucial to fully leveraging the value of this information resource. This study develops a two-phase method to identify key predictors for ARQ forecasting. In the first stage, we conduct a thorough literature review to obtain a comprehensive list of candidate features, and organise them under three categories: analyst-related, rating-related, and stock-related. In the second stage, we propose a heterogeneous community-based ensemble feature selection method (ComEFS), with the goal of identifying a subset of relevant predictors to be jointly used for ARQ forecasting. Thorough experiments are conducted on a real dataset to verify the effectiveness of our proposed method. The empirical results show that key predictors identified by ComEFS exhibit stronger predictive power compared to those identified by benchmark methods. This study provides insights about ARQ forecasting by selecting the right input. Selectively utilizing these predictive features can help improve the performance of downstream machine learning models and ultimately help investors avoid unreliable analyst ratings and financial loss.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"39 4","pages":"Pages 1853-1873"},"PeriodicalIF":7.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44209332","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

书评

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-10-01 DOI: 10.1016/j.ijforecast.2022.12.002

Mahdi Abolghasemi

引用次数: 0

Dynamic linear models with adaptive discounting 具有自适应折扣的动态线性模型

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-10-01 DOI: 10.1016/j.ijforecast.2022.09.006

Alisa Yusupova , Nicos G. Pavlidis , Efthymios G. Pavlidis

引用次数: 0

Early Warning Systems for identifying financial instability 识别金融不稳定的预警系统

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-10-01 DOI: 10.1016/j.ijforecast.2022.08.004

Erindi Allaj, Simona Sanfelici

引用次数: 1