International Journal of Forecasting最新文献_第8页

Properties of the reconciled distributions for Gaussian and count forecasts 高斯预测和计数预测的协调分布特性

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-01-12 DOI: 10.1016/j.ijforecast.2023.12.004

引用次数: 0

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2024-01-08 DOI: 10.1016/j.ijforecast.2023.12.001

引用次数: 0

Thinking outside the container: A sparse partial least squares approach to forecasting trade flows 集装箱外的思考：预测贸易流量的稀疏偏最小二乘法

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-01-04 DOI: 10.1016/j.ijforecast.2023.11.007

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引用次数: 0

Forecasting day-ahead expected shortfall on the EUR/USD exchange rate: The (I)relevance of implied volatility 预测欧元/美元汇率的当日预期缺口：隐含波动率的（I）相关性

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-01-04 DOI: 10.1016/j.ijforecast.2023.11.003

{"title":"Forecasting day-ahead expected shortfall on the EUR/USD exchange rate: The (I)relevance of implied volatility","authors":"","doi":"10.1016/j.ijforecast.2023.11.003","DOIUrl":"10.1016/j.ijforecast.2023.11.003","url":null,"abstract":"<div><p><span><span>The existing literature provides mixed results on the usefulness of implied volatility for managing risky assets, while evidence for expected shortfall predictions is almost nonexistent. Given its forward-looking nature, implied volatility might be more valuable than backward-looking measures of realized price fluctuations. Conversely, the volatility risk premium embedded in implied volatility leads to overestimating the observed price variation. This paper explores the benefits of augmenting econometric models used in forecasting the expected shortfall, a risk measured endorsed in the Basel III Accord, with information on implied volatility obtained from EUR/USD </span>option contracts<span>. The day-ahead forecasts are obtained from several classes of econometric models: historical simulation, EGARCH, </span></span>quantile regression-based HAR, joint VaR and ES model, and combination forecasts. We verify whether the resulting expected shortfall forecasts are well-specified and test the models’ accuracy. Our results provide evidence that the information provided by forward-looking implied volatility is more valuable than that in backward-looking realized measures. These results hold across multiple model specifications, are stable over time, hold under alternative loss functions, and are more pronounced during periods of higher market uncertainty when risk modeling matters most.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1275-1301"},"PeriodicalIF":6.9,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139374581","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

A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market 澳大利亚全国电力市场波动电价的概率预测方法

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-01-03 DOI: 10.1016/j.ijforecast.2023.12.003

引用次数: 0

Forecast reconciliation: A review 预测调节：回顾

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-12-29 DOI: 10.1016/j.ijforecast.2023.10.010

George Athanasopoulos , Rob J. Hyndman , Nikolaos Kourentzes , Anastasios Panagiotelis

{"title":"Forecast reconciliation: A review","authors":"George Athanasopoulos , Rob J. Hyndman , Nikolaos Kourentzes , Anastasios Panagiotelis","doi":"10.1016/j.ijforecast.2023.10.010","DOIUrl":"10.1016/j.ijforecast.2023.10.010","url":null,"abstract":"<div><p>Collections of time series formed via aggregation are prevalent in many fields. These are commonly referred to as hierarchical time series and may be constructed cross-sectionally across different variables, temporally by aggregating a single series at different frequencies, or even generalised beyond aggregation as time series that respect linear constraints. When forecasting such time series, a desirable condition is for forecasts to be coherent: to respect the constraints. The past decades have seen substantial growth in this field with the development of reconciliation methods that ensure coherent forecasts and improve forecast accuracy. This paper serves as a comprehensive review of forecast reconciliation and an entry point for researchers and practitioners dealing with hierarchical time series. The scope of the article includes perspectives on forecast reconciliation from machine learning, Bayesian statistics and probabilistic forecasting, as well as applications in economics, energy, tourism, retail demand and demography.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 2","pages":"Pages 430-456"},"PeriodicalIF":7.9,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023001097/pdfft?md5=22d99799cd25ab98a5a7ae5145f1d7e2&pid=1-s2.0-S0169207023001097-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139071694","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

Generalized Poisson difference autoregressive processes 广义泊松差分自回归过程

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2023-12-28 DOI: 10.1016/j.ijforecast.2023.11.009

{"title":"Generalized Poisson difference autoregressive processes","authors":"","doi":"10.1016/j.ijforecast.2023.11.009","DOIUrl":"10.1016/j.ijforecast.2023.11.009","url":null,"abstract":"<div><p>This paper introduces a novel stochastic process with signed integer values. Its autoregressive dynamics effectively captures persistence in conditional moments, rendering it a valuable feature for forecasting applications. The increments follow a Generalized Poisson distribution, capable of accommodating over- and under-dispersion in the conditional distribution, thereby extending standard Poisson difference models. We derive key properties of the process, including stationarity conditions, the stationary distribution, and conditional and unconditional moments, which prove essential for accurate forecasting. We provide a Bayesian inference framework with an efficient posterior approximation based on Markov Chain Monte Carlo. This approach seamlessly incorporates inherent parameter uncertainty into predictive distributions. The effectiveness of the proposed model is demonstrated through applications to benchmark datasets on car accidents and an original dataset on cyber threats, highlighting its superior fitting and forecasting capabilities compared to standard Poisson models.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1359-1390"},"PeriodicalIF":6.9,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023001310/pdfft?md5=4c712880186f559616263672436b5004&pid=1-s2.0-S0169207023001310-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139071376","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 emergency department occupancy with advanced machine learning models and multivariable input 利用先进的机器学习模型和多变量输入预测急诊室占用率

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2023-12-27 DOI: 10.1016/j.ijforecast.2023.12.002

{"title":"Forecasting emergency department occupancy with advanced machine learning models and multivariable input","authors":"","doi":"10.1016/j.ijforecast.2023.12.002","DOIUrl":"10.1016/j.ijforecast.2023.12.002","url":null,"abstract":"<div><p>Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand has the potential to improve patient outcomes. Despite active research on the subject, proposed forecasting models have become outdated, due to the quick influx of advanced machine learning models and because the amount of multivariable input data has been limited. In this study, we document the performance of a set of advanced machine learning models in forecasting ED occupancy 24 h ahead. We use electronic health record data from a large, combined ED with an extensive set of explanatory variables, including the availability of beds in catchment area hospitals, traffic data from local observation stations, weather variables, and more. We show that DeepAR, N-BEATS, TFT, and LightGBM all outperform traditional benchmarks, with up to 15% improvement. The inclusion of the explanatory variables enhances the performance of TFT and DeepAR but fails to significantly improve the performance of LightGBM. To the best of our knowledge, this is the first study to extensively document the superiority of machine learning over statistical benchmarks in the context of ED forecasting.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1410-1420"},"PeriodicalIF":6.9,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023001346/pdfft?md5=ce6f2f913f2f56e0a000145a128a4966&pid=1-s2.0-S0169207023001346-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139054325","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

An assessment of the marginal predictive content of economic uncertainty indexes and business conditions predictors 经济不确定性指数和商业条件预测器的边际预测内容评估

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2023-12-22 DOI: 10.1016/j.ijforecast.2023.11.010

{"title":"An assessment of the marginal predictive content of economic uncertainty indexes and business conditions predictors","authors":"","doi":"10.1016/j.ijforecast.2023.11.010","DOIUrl":"10.1016/j.ijforecast.2023.11.010","url":null,"abstract":"<div><p><span>In this paper, we evaluate the marginal predictive content of a variety of new business conditions (BC) predictors as well as nine economic uncertainty indexes (EUIs) constructed using these predictors. Our predictors are defined as observable variables and latent factors extracted from a high-dimensional macroeconomic dataset, and our EUIs are functions of predictive errors from models that incorporate these predictors. Estimation of the predictors is based on a number of extant and novel machine learning methods that combine dimension reduction, variable selection, and shrinkage. When predicting 14 monthly </span>U.S. economic series selected from eight different groups of economic variables, our new indexes and predictors are shown to result in significant improvements in forecast accuracy relative to predictions made using benchmark models. In particular, inclusion of either BC predictors or EUIs often yields forecast accuracy improvements, while even greater predictive gains accrue when including both BC predictors and EUIs when forecasting real economic activity-type variables at shorter forecast horizons.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1391-1409"},"PeriodicalIF":6.9,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139015488","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

An assessment of the marginal predictive content of economic uncertainty indexes and business conditions predictors 经济不确定性指数和商业条件预测器的边际预测内容评估

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-12-22 DOI: 10.1016/j.ijforecast.2023.11.010

Yang Liu, Norman R. Swanson

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引用次数: 0