Journal of Forecasting最新文献_第6页

A Review of Methods for Long-Term Electric Load Forecasting 电力负荷长期预测方法综述

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-12-26 DOI: 10.1002/for.3248

Thangjam Aditya, Sanjita Jaipuria, Pradeep Kumar Dadabada

{"title":"A Review of Methods for Long-Term Electric Load Forecasting","authors":"Thangjam Aditya, Sanjita Jaipuria, Pradeep Kumar Dadabada","doi":"10.1002/for.3248","DOIUrl":"https://doi.org/10.1002/for.3248","url":null,"abstract":"<div>\u0000 \u0000 <p>Long-term load forecasting (LTLF) has been a fundamental least-cost planning tool for electric utilities. In the past, utilities were monopolies and paid less attention to uncertainty in their LTLF methodologies. Nowadays, such casualness is pricey in competitive markets because utilities need to examine the financial implications of forecast uncertainty for survival. Hence, the aim of this paper is to critique the LTLF research trends with a focus on uncertainty quantification (UQ). For this purpose, we examined 40 LTLF articles published between January 2003 and February 2021. We found that UQ is a nascent area of LTLF research. Our review found two approaches to UQ in LTLF: probabilistic scenario analysis and direct probabilistic methods. The former approach is more helpful to risk analysts but has major caveats in addressing interdependencies of socioeconomic and climate scenarios. We identified very little LTLF research that examines uncertainties associated with climate extremes, distributed generation resources, and demand-side management. Lastly, there is enormous potential for mitigating financial risks by embracing asymmetric cost functions in LTLF research. Future LTLF researchers can work on these identified gaps to help utilities in risk estimation, cost-reliability balancing, and estimation of reserve margin under climate change.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1403-1423"},"PeriodicalIF":3.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206986","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

Money Matters: Broad Divisia Money and the Recovery of the US Nominal GDP From the COVID-19 Recession 货币问题：广义货币与美国名义GDP从COVID-19衰退中复苏

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-12-24 DOI: 10.1002/for.3242

Michael D. Bordo, John V. Duca

{"title":"Money Matters: Broad Divisia Money and the Recovery of the US Nominal GDP From the COVID-19 Recession","authors":"Michael D. Bordo, John V. Duca","doi":"10.1002/for.3242","DOIUrl":"https://doi.org/10.1002/for.3242","url":null,"abstract":"<div>\u0000 \u0000 <p>The rise of inflation in 2021 and 2022 surprised many macroeconomists who ignored the earlier surge in money growth because of past instability in the demand for simple-sum monetary aggregates. We find that the demand for more theoretically based Divisia aggregates can be modeled and that these aggregates provide useful information about nominal GDP. Unlike M2 and Divisia-M2, whose velocities do not internalize shifts in liabilities across commercial and shadow banks, the velocities of broader Divisia monetary aggregates are stable and can be empirically modeled through the Covid-19 pandemic. In the long run, these velocities depend on regulation and mutual fund costs that affect the substitutability of money for other financial assets. In the short run, we control for swings in mortgage activity and use vaccination rates and the stringency of government pandemic restrictions to control for the unusual pandemic effects. The velocity of broad Divisia money declines during crises like the Great and COVID Recessions but later rebounds. In these recessions, monetary policy lowered short-term interest rates to zero and engaged in quantitative easing of about $4 trillion. Nevertheless, broad money growth was more robust in the COVID Recession, reflecting a less impaired banking system that promoted rather than hindered deposit creation. Our framework implies that nominal GDP growth and inflation rebounded more quickly from the COVID Recession versus the Great Recession. Our different scenarios for future Divisia money growth and the unwinding of the pandemic have different implications for medium-term nominal GDP growth and inflationary pressures.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"1071-1096"},"PeriodicalIF":3.4,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143565390","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

Explainable Soybean Futures Price Forecasting Based on Multi-Source Feature Fusion 基于多源特征融合的可解释大豆期货价格预测

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-12-24 DOI: 10.1002/for.3246

Binrong Wu, Sihao Yu, Sheng-Xiang Lv

{"title":"Explainable Soybean Futures Price Forecasting Based on Multi-Source Feature Fusion","authors":"Binrong Wu, Sihao Yu, Sheng-Xiang Lv","doi":"10.1002/for.3246","DOIUrl":"https://doi.org/10.1002/for.3246","url":null,"abstract":"<div>\u0000 \u0000 <p>The prediction and early warning of soybean futures prices have been even more crucial for the formulation of food-related policies and trade risk management. Amid increasing geopolitical conflicts and uncertainty in trade policies across countries in recent years, there have been significant fluctuations in global soybean futures prices, making it necessary to investigate fluctuations in soybean futures prices, reveal the price determination mechanism, and accurately predict trends in future prices. Therefore, this study proposes a comprehensive and interpretable framework for soybean futures price forecasting. Specifically, this study employs a set of methodologies. Using a snow ablation optimizer (SAO), this study improves the parameters of a time fusion transformer (TFT) model, an advanced interpretable predictive model based on a self-attention mechanism. Besides, it addresses the factors influencing soybean futures prices and constructs effective fusion features through a feature fusion method. To explore volatility trends, the original soybean futures price series are decomposed using variational mode decomposition (VMD). This study also enhances the accuracy of soybean futures price predictions by introducing global geopolitical risk coefficients and trading volumes as predictors. The empirical findings suggest that the VMD-SAO-TFT model enhances prediction accuracy and interpretability, offering implications for decision-makers to achieve accurate predictions and early warning of agricultural futures prices.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1363-1382"},"PeriodicalIF":3.4,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206967","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

Forecasting the Volatility of US Oil and Gas Firms With Machine Learning 用机器学习预测美国油气公司的波动性

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-12-24 DOI: 10.1002/for.3245

Juan D. Díaz, Erwin Hansen, Gabriel Cabrera

引用次数: 0

Nonstationary Functional Time Series Forecasting 非平稳函数时间序列预测

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-12-17 DOI: 10.1002/for.3241

Han Lin Shang, Yang Yang

引用次数: 0

Common Mutual Information Selection Algorithm and Its Application on Combination Forecasting 公共互信息选择算法及其在组合预测中的应用

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-12-11 DOI: 10.1002/for.3240

Chenqing Shen, Huayou Chen

{"title":"Common Mutual Information Selection Algorithm and Its Application on Combination Forecasting","authors":"Chenqing Shen, Huayou Chen","doi":"10.1002/for.3240","DOIUrl":"https://doi.org/10.1002/for.3240","url":null,"abstract":"<div>\u0000 \u0000 <p>The subset selection of individual prediction methods is gradually becoming a hot topic. Among numerous forecasts, identifying the optimal subset approach has become a major focal point of research. To address this issue, the paper introduces a novel method based on information theory, which is called common mutual information (CMI) selection algorithm. This optimal subset selection method not only simultaneously considers the relationships of three factors, which include the candidate feature set, the selected feature set, and the actual time series, but also provides a more precise treatment of these relationships. Therefore, CMI algorithm employs the mutual information (MI) shared among the three factors as the criterion for selection and improves the accuracy of the redundancy or correlation measure for existing algorithms. Furthermore, it overcomes the deficiency of calculating MI between the candidate subset and the actual time series. Existing algorithms use the average MI values between individual elements within the subset and the actual sequence; this paper takes the selected subset as a multidimensional input for MI computation, thus reducing computational errors. Finally, the proposed algorithm is compared with two other approaches of the MI algorithm, the Max-Relevance and Min-Redundancy (mRMR) algorithm in both theoretical and empirical aspects. The experiments are illustrated to show the effectiveness and superiority of CMI algorithm.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1326-1346"},"PeriodicalIF":3.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206455","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

Forecasting the Realized Volatility of Stock Markets: The Roles of Jumps and Asymmetric Spillovers 股票市场已实现波动率的预测：跳跃和非对称溢出的作用

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-12-10 DOI: 10.1002/for.3219

Abdel Razzaq Al Rababaa, Walid Mensi, David McMillan, Sang Hoon Kang

{"title":"Forecasting the Realized Volatility of Stock Markets: The Roles of Jumps and Asymmetric Spillovers","authors":"Abdel Razzaq Al Rababaa, Walid Mensi, David McMillan, Sang Hoon Kang","doi":"10.1002/for.3219","DOIUrl":"https://doi.org/10.1002/for.3219","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper evaluates the roles of jump and sign-asymmetry spillovers in forecasting the realized volatility in a large sample of 20 stock markets. We compare for the first time whether controlling for either the jumps or asymmetric spillovers into the heterogeneous autoregressive–realized volatility (HAR-RV) model improves the forecasts over 1, 5 and 22 days. Before doing so, the spillovers predictors are generated. In analyzing the spillover process, we find that the US stock market remains the main net transmitter of shocks, and while China is relatively detached from the spillover linkages, such effects may be transmitted through Hong Kong, which is a significant receiver of shocks. The out-of-sample results reveal that the incorporation of jump spillovers improves forecast performance the most across a range of measures. This is more clearly demonstrated at the 22-day forecasting horizon more notably in Europe, France, Germany, India, and the United Kingdom. Lastly, irrespective of the forecasting horizon, performing the predicting stability test uncovers significant improvements in the jump spillover–based model during periods of notable market stress such as the 2014–2016 oil price crash and COVID-19. Overall, results suggest paying more attention to jump spillover while constructing international portfolios based on the realized volatility.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1294-1325"},"PeriodicalIF":3.4,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206699","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

On the Detection of Structural Breaks: The Case of the Covid Shock 论结构性断裂的检测：以新冠肺炎冲击为例

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-12-09 DOI: 10.1002/for.3238

Stephen G. Hall, George S. Tavlas, Lorenzo Trapani, Yongli Wang

{"title":"On the Detection of Structural Breaks: The Case of the Covid Shock","authors":"Stephen G. Hall, George S. Tavlas, Lorenzo Trapani, Yongli Wang","doi":"10.1002/for.3238","DOIUrl":"https://doi.org/10.1002/for.3238","url":null,"abstract":"<div>\u0000 \u0000 <p>Both the Federal Reserve (Fed) and the European Central Bank (ECB) have been criticized for not having perceived that the outbreak of Covid at the beginning of 2020 would lead to a structural change in inflation in the early 2020s. Both central banks viewed the initial inflation surge in 2021 as temporary and delayed monetary tightening until 2022. We argue that the existing literature on structural breaks could not have been useful to policymakers because it identifies the breaks in an arbitrary way. The tests used to identify breaks do not incorporate prior knowledge that a break may have occurred so that the tests have very little power to detect a break that occurs at the end of the sample. We show that, in the event of a major shock, such as Covid, using knowledge that a break may have occurred and testing for a break in a recursive way as new data become available could have alerted policymakers to the break in inflation. We conduct Monte Carlo simulations suggesting that our method would have identified that a break had occurred in inflation by the end of 2020, well before policymakers had perceived the break.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"1042-1070"},"PeriodicalIF":3.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143565332","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

Step by Step—A Quarterly Evaluation of EU Commission's GDP Forecasts 一步一步：欧盟委员会GDP预测季度评估

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-12-09 DOI: 10.1002/for.3226

Katja Heinisch

{"title":"Step by Step—A Quarterly Evaluation of EU Commission's GDP Forecasts","authors":"Katja Heinisch","doi":"10.1002/for.3226","DOIUrl":"https://doi.org/10.1002/for.3226","url":null,"abstract":"<p>The European Commission's growth forecasts play a crucial role in shaping policies and provide a benchmark for many (national) forecasters. The annual forecasts are built on quarterly estimates, which do not receive much attention and are hardly known. Therefore, this paper provides a comprehensive analysis of multiperiod ahead quarterly GDP growth forecasts for the European Union (EU), euro area, and several EU member states with respect to first-release and current-release data. Forecast revisions and forecast errors are analyzed, and the results show that the forecasts are not systematically biased. However, GDP forecasts for several member states tend to be overestimated at short-time horizons. Furthermore, the final forecast revision in the current quarter is generally downward biased for almost all countries. Overall, the differences in mean forecast errors are minor when using real-time data or pseudo–real-time data and these differences do not significantly impact the overall assessment of the forecasts' quality. Additionally, the forecast performance varies across countries, with smaller countries and Central and Eastern European countries (CEECs) experiencing larger forecast errors. The paper provides evidence that there is still potential for improvement in forecasting techniques both for nowcasts but also forecasts up to eight quarters ahead. In the latter case, the performance of the mean forecast tends to be superior for many countries.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"1026-1041"},"PeriodicalIF":3.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3226","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143565334","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

Extended Multivariate EGARCH Model: A Model for Zero-Return and Negative Spillovers 扩展多元EGARCH模型：一个零收益和负溢出的模型

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-12-08 DOI: 10.1002/for.3243

Yongdeng Xu

{"title":"Extended Multivariate EGARCH Model: A Model for Zero-Return and Negative Spillovers","authors":"Yongdeng Xu","doi":"10.1002/for.3243","DOIUrl":"https://doi.org/10.1002/for.3243","url":null,"abstract":"<p>This paper introduces an extended multivariate EGARCH model that overcomes the zero-return problem and allows for negative news and volatility spillover effects, making it an attractive tool for multivariate volatility modeling. Despite limitations, such as noninvertibility and unclear asymptotic properties of the QML estimator, our Monte Carlo simulations indicate that the standard QML estimator is consistent and asymptotically normal for larger sample sizes (i.e., \u0000<span></span><math>\u0000 <mi>T</mi>\u0000 <mo>≥</mo>\u0000 <mn>2500</mn></math>). Two empirical examples demonstrate the model's superior performance compared to multivariate GJR-GARCH and Log-GARCH models in volatility modeling. The first example analyzes the daily returns of three stocks from the DJ30 index, while the second example investigates volatility spillover effects among the bond, stock, crude oil, and gold markets. Overall, this extended multivariate EGARCH model offers a flexible and comprehensive framework for analyzing multivariate volatility and spillover effects in empirical finance research.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1266-1279"},"PeriodicalIF":3.4,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3243","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206863","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