Journal of Forecasting最新文献_第9页

Credit card loss forecasting: Some lessons from COVID 信用卡损失预测：COVID 的一些经验教训

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-23 DOI: 10.1002/for.3137

Partha Sengupta, Christopher H. Wheeler

{"title":"Credit card loss forecasting: Some lessons from COVID","authors":"Partha Sengupta, Christopher H. Wheeler","doi":"10.1002/for.3137","DOIUrl":"10.1002/for.3137","url":null,"abstract":"Models developed by banks to forecast losses in their credit card portfolios have generally performed poorly during the COVID-19 pandemic, particularly in 2020, when large forecast errors were observed at many banks. In this study, we attempt to understand the source of this error and explore ways to improve model fit. We use account-level monthly performance data from the largest credit card banks in the U.S. between 2008 and 2018 to build models that mimic the typical model design employed by large banks to forecast credit card losses. We then fit these on data from 2019 to 2021. We find that COVID-period model errors can be reduced significantly through two simple modifications: (1) including measures of the macroeconomic environment beyond indicators of the labor market, which served as the primary macro drivers used in many pre-pandemic models and (2) adjusting macro drivers to capture persistent/sustained changes, as opposed to temporary volatility in these variables. These model improvements, we find, can be achieved without a significant reduction in model performance for the pre-COVID period, including the Great Recession. Moreover, in broadening the set of macro influences and capturing sustained changes, we believe models can be made more robust to future downturns, which may bear little resemblance to past recessions.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2448-2477"},"PeriodicalIF":3.4,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Prediction of wind energy with the use of tensor-train based higher order dynamic mode decomposition 利用基于张量列车的高阶动态模式分解预测风能

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-14 DOI: 10.1002/for.3126

Keren Li, Sergey Utyuzhnikov

引用次数: 0

Multivariable forecasting approach of high-speed railway passenger demand based on residual term of Baidu search index and error correction 基于百度搜索指数残差项和误差修正的高速铁路客运需求多变量预测方法

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-14 DOI: 10.1002/for.3134

Hongtao Li, Xiaoxuan Li, Shaolong Sun, Zhipeng Huang, Xiaoyan Jia

{"title":"Multivariable forecasting approach of high-speed railway passenger demand based on residual term of Baidu search index and error correction","authors":"Hongtao Li, Xiaoxuan Li, Shaolong Sun, Zhipeng Huang, Xiaoyan Jia","doi":"10.1002/for.3134","DOIUrl":"10.1002/for.3134","url":null,"abstract":"Accurate prior information of passenger flow demand on high-speed railway is of great significance for the operation and the management of transportation systems. Various factors in modern social life have caused uncertainty at demand. Recently, individuals are increasingly depending on the online search results when choosing among different transportation modes, services, and destinations, which provide important basic information for forecasting the travel demand. This study employs Baidu search index to assist in capturing volatility of high-speed railway passenger demands, offering insights into the travel inclinations and travelers' actions. Furthermore, we have given more in-depth attention and analysis to their residual term accounting for the random nature caused by various factors. To this end, a sophisticated deep analysis mechanism based on data decomposition has been devised to extract and analyze the valuable information concealed within the residuals, so as to enhance the comprehension of the variability inherent in the high-speed railway passenger flow. Meanwhile, an error correction strategy is implemented for all residual terms to improve further their forecasting accuracy. Experimental results from two real-world datasets demonstrate the effectiveness and robustness of the developed hybrid approach across several popular evaluation indicators. Therefore, this study can function as a reliable instrument, provide sensible data-driven guidance for resource allocation and make scientific decisions in the railway industry.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2401-2433"},"PeriodicalIF":3.4,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Hybrid forecasting of crude oil volatility index: The cross-market effects of stock market jumps 原油波动指数的混合预测：股市跳跃的跨市场效应

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-11 DOI: 10.1002/for.3132

Gongyue Jiang, Gaoxiu Qiao, Lu Wang, Feng Ma

引用次数: 0

Are national or regional surveys useful for nowcasting regional jobseekers? The case of the French region of Pays-de-la-Loire 国家或地区调查对预测地区求职者是否有用？以法国卢瓦尔河地区为例

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-11 DOI: 10.1002/for.3125

Clément Cariou, Amélie Charles, Olivier Darné

引用次数: 0

Forecasting healthcare service volumes with machine learning algorithms 利用机器学习算法预测医疗服务量

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-11 DOI: 10.1002/for.3133

Dong-Hui Yang, Ke-Hui Zhu, Ruo-Nan Wang

{"title":"Forecasting healthcare service volumes with machine learning algorithms","authors":"Dong-Hui Yang, Ke-Hui Zhu, Ruo-Nan Wang","doi":"10.1002/for.3133","DOIUrl":"10.1002/for.3133","url":null,"abstract":"As an efficacious solution to remedying the imbalance of medical resources, the online medical platform has burgeoned expeditiously. Apt allotment of medical resources on the medical platform can facilitate patients in reasonably selecting physicians and time slots, coordinating doctors' clinical arrangements, and generating precise projections of medical platform service volume to enhance patient satisfaction and alleviate physicians' workload. To this end, grounded in the data-driven method, this paper assembles an exhaustive feature set encompassing hospital features, physician features, and patient features. Through feature selection, appropriate features are screened, and machine learning algorithms are leveraged to accurately forecast doctors' online consultation volume. Subsequently, to glean the influence relationship between online medical services and offline medical services, this paper introduces features of offline medical services such as hospital registration volume and regional gross domestic product (GDP) to solve the prediction of offline medical service volume using online medical information. The findings signify that online data feature prediction can pinpoint superior machine learning models for online medical platform service volume (with the optimal accuracy up to 96.89%). Online features exert a positive effect on predicting offline medical service volume, but the accuracy declines to some degree (the optimal accuracy is 73%). Physicians with favorable reputations on the online platform are more susceptible to attain higher offline appointment volumes when online consultation volume is a vital feature impacting offline appointment volume.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 6","pages":"2358-2377"},"PeriodicalIF":3.4,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Bayesian Markov switching model for BRICS currencies' exchange rates 金砖国家货币汇率的贝叶斯马尔科夫转换模型

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-09 DOI: 10.1002/for.3128

Utkarsh Kumar, Wasim Ahmad, Gazi Salah Uddin

{"title":"Bayesian Markov switching model for BRICS currencies' exchange rates","authors":"Utkarsh Kumar, Wasim Ahmad, Gazi Salah Uddin","doi":"10.1002/for.3128","DOIUrl":"https://doi.org/10.1002/for.3128","url":null,"abstract":"Exchange rate modeling has always fascinated researchers because of its complex macroeconomic dynamics. This study documents the exchange rate dynamics of major emerging economies after accounting for their macroeconomic cycles and explores the Bayesian Vector Error Correction Model (VECM) Markov Regime switching model, which uses time-varying transition probabilities. The main objective is to study the exchange rate dynamics of Brazil, Russia, India, China, and South Africa (BRICS) vis-à-vis the US dollar. The Bayesian setup uses two hierarchal shrinkage priors, the normal-gamma (NG) prior and the Litterman prior, for parameters' estimation. These shrinkage priors allow for a more comprehensive assessment of the regime-specific coefficients. The model performed well in differentiating between the two regimes for all currencies. The Russian ruble was identified to be the most depreciated currency, whereas the African Rand was the most appreciated. The evaluation of model features revealed that many regime-specific coefficients differed significantly from their common mean. A forecasting exercise was then performed for the out-of-sample period to assess the model's performance. A significant improvement was observed over the basic random walk (RW) model and the linear Bayesian vector autoregression (BVAR) model.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 6","pages":"2322-2340"},"PeriodicalIF":3.4,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

The mean squared prediction error paradox 均方预测误差悖论

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-08 DOI: 10.1002/for.3129

Pablo Pincheira Brown, Nicolás Hardy

引用次数: 0

A systematic vector autoregressive framework for modeling and forecasting mortality 用于模拟和预测死亡率的系统向量自回归框架

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-08 DOI: 10.1002/for.3127

Jackie Li, Jia Liu, Adam Butt

{"title":"A systematic vector autoregressive framework for modeling and forecasting mortality","authors":"Jackie Li, Jia Liu, Adam Butt","doi":"10.1002/for.3127","DOIUrl":"https://doi.org/10.1002/for.3127","url":null,"abstract":"Recently, there is a new stream of mortality forecasting research using the vector autoregressive model with different sparse model specifications. They have been shown to be able to overcome some of the limitations of the more traditional factor models such as the Lee–Carter model. In this paper, we propose a more generalized systematic vector autoregressive framework for modeling and forecasting mortality. Under this framework, we progressively increase the sophistication of the diagonal parameters in the autoregressive matrix and formulate a range of model structures in a systematic fashion. They offer much flexibility for capturing the mortality patterns of different populations. The resulting models produce age coherent forecasts, and their parameters are reasonably interpretable for modelers, demographers, and industry practitioners. Using the mortality data of Australia, Japan, New Zealand, and Taiwan, we demonstrate that the proposed approach generates appropriate forecasts of mortality rates and life expectancies and produces very good performance in the fitting and out-of-sample analysis.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 6","pages":"2279-2297"},"PeriodicalIF":3.4,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 carbon emissions using asymmetric grouping 利用非对称分组预测碳排放量

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-04 DOI: 10.1002/for.3124

Didier Nibbering, Richard Paap

引用次数: 0