International Journal of Forecasting最新文献_第7页

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

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-11-07 DOI: 10.1016/j.ijforecast.2023.11.001

Fred Collopy, Robert Fildes

引用次数: 0

Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues 跨时概率预测协调：方法和实践问题

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-11-07 DOI: 10.1016/j.ijforecast.2023.10.003

Daniele Girolimetto , George Athanasopoulos , Tommaso Di Fonzo , Rob J. Hyndman

{"title":"Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues","authors":"Daniele Girolimetto , George Athanasopoulos , Tommaso Di Fonzo , Rob J. Hyndman","doi":"10.1016/j.ijforecast.2023.10.003","DOIUrl":"10.1016/j.ijforecast.2023.10.003","url":null,"abstract":"<div><p>Forecast reconciliation is a post-forecasting process that involves transforming a set of incoherent forecasts into coherent forecasts which satisfy a given set of linear constraints for a multivariate time series. In this paper, we extend the current state-of-the-art cross-sectional probabilistic forecast reconciliation approach to encompass a cross-temporal framework, where temporal constraints are also applied. Our proposed methodology employs both parametric Gaussian and non-parametric bootstrap approaches to draw samples from an incoherent cross-temporal distribution. To improve the estimation of the forecast error covariance matrix, we propose using multi-step residuals, especially in the time dimension where the usual one-step residuals fail. To address high-dimensionality issues, we present four alternatives for the covariance matrix, where we exploit the two-fold nature (cross-sectional and temporal) of the cross-temporal structure, and introduce the idea of overlapping residuals. We assess the effectiveness of the proposed cross-temporal reconciliation approaches through a simulation study that investigates their theoretical and empirical properties and two forecasting experiments, using the Australian GDP and the Australian Tourism Demand datasets. For both applications, the optimal cross-temporal reconciliation approaches significantly outperform the incoherent base forecasts in terms of the continuous ranked probability score and the energy score. Overall, the results highlight the potential of the proposed methods to improve the accuracy of probabilistic forecasts and to address the challenge of integrating disparate scenarios while coherently taking into account short-term operational, medium-term tactical, and long-term strategic planning.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 3","pages":"Pages 1134-1151"},"PeriodicalIF":7.9,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023001024/pdfft?md5=b9978f6e8d4d5d5d9fff37d9d9c92f92&pid=1-s2.0-S0169207023001024-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135509503","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

Evaluating probabilistic classifiers: The triptych 评估概率分类器：三部曲

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-11-04 DOI: 10.1016/j.ijforecast.2023.09.007

Timo Dimitriadis , Tilmann Gneiting , Alexander I. Jordan , Peter Vogel

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引用次数: 0

Short-term stock price trend prediction with imaging high frequency limit order book data 利用成像高频限价订单簿数据预测短期股价趋势

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-11-03 DOI: 10.1016/j.ijforecast.2023.10.008

Wuyi Ye, Jinting Yang, Pengzhan Chen

引用次数: 0

DeepTVAR: Deep learning for a time-varying VAR model with extension to integrated VAR DeepTVAR：时变 VAR 模型的深度学习，扩展至综合 VAR

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-10-30 DOI: 10.1016/j.ijforecast.2023.10.001

Xixi Li, Jingsong Yuan

引用次数: 0

Obituary: Everette S Gardner Jr 讣告:小埃弗雷特·S·加德纳

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-10-28 DOI: 10.1016/j.ijforecast.2023.10.006

Robert Fildes, Rob J Hyndman

引用次数: 0

Out-of-sample predictability in predictive regressions with many predictor candidates 有许多候选预测因子的预测性回归中的样本外可预测性

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2023-10-28 DOI: 10.1016/j.ijforecast.2023.10.005

Jesús Gonzalo , Jean-Yves Pitarakis

引用次数: 0