Journal of Forecasting最新文献

{"title":"Processes and Predictions in Ecological Models: Logic and Causality","authors":"Christian Damgaard","doi":"10.1002/for.3267","DOIUrl":"https://doi.org/10.1002/for.3267","url":null,"abstract":"<p>To make credible ecological predictions for terrestrial ecosystems in a changing environment and increase our understanding of ecological processes, we need plant ecological models that can be fitted to spatial and temporal ecological data. Such models need to be based on a sufficient understanding of ecological processes to make credible predictions and account for the different sources of uncertainty. Here, I argue (1) for the use of structural equation models in a hierarchical framework with latent variables and (2) to specify whether our current knowledge of relationships among state variables may be categorized primarily as logical (empirical) or causal. Such models will help us to make continuous progress in our understanding of and ability to predict the dynamics of terrestrial ecosystems and provide us with local predictions with a known degree of uncertainty that are useful for generating adaptive management plans. The hierarchical structural equation models I recommend are analogous to current general epistemological models of how knowledge is obtained.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 5","pages":"1658-1665"},"PeriodicalIF":3.4,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3267","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144525097","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}

{"title":"Modeling and Forecasting the CBOE VIX With the TVP-HAR Model","authors":"Wen Xu,&nbsp;Pakorn Aschakulporn,&nbsp;Jin E. Zhang","doi":"10.1002/for.3260","DOIUrl":"https://doi.org/10.1002/for.3260","url":null,"abstract":"<p>This study proposes the use of a heterogeneous autoregressive model with time-varying parameters (TVP-HAR) to model and forecast the Chicago Board Options Exchange (CBOE) volatility index (VIX). To demonstrate the superiority of the TVP-HAR model, we consider six variations of the model with different bandwidths and smoothing variables and include the constant-coefficient HAR model as a benchmark for comparison. We show that the TVP-HAR models could beat the HAR model with constant coefficients in modeling and forecasting VIX. Among the TVP-HAR models, the rule-of-thumb bandwidth would be better than the cross-validation bandwidth. Meanwhile, VIX futures-driven coefficients could also provide more accurate predictions and smaller capital losses than the other two variables. Overall, the VIX futures-driven coefficients TVP-HAR model with the rule-of-thumb bandwidth obtains the optimal result for investors in forecasting the market risks and shaping their hedging strategies.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 5","pages":"1638-1657"},"PeriodicalIF":3.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3260","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524776","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}

{"title":"Policymaking in Periods of Structural Changes and Structural Breaks: Rolling Windows Revisited","authors":"Nikolaos Giannellis,&nbsp;Stephen G. Hall,&nbsp;Georgios P. Kouretas,&nbsp;George S. Tavlas,&nbsp;Yongli Wang","doi":"10.1002/for.3269","DOIUrl":"https://doi.org/10.1002/for.3269","url":null,"abstract":"<div>\u0000 \u0000 <p>Early studies that used rolling windows found it to be a useful forecasting technique. These studies were, by-and-large, based on pre-2000 data, which were nonstationary. Subsequent work, based on stationary data from the mid-1990s to 2020, has not been able to confirm that finding. However, this latter result may reflect the fact that there was relatively little structural instability between the mid-1990s and 2020: The data had become stationary. Following the series of shocks of the early 2020s, this is no longer the case because the shocks produced nonstationarity in the macroeconomic data, such as inflation. Consequently, rolling windows may again be a sensible way forward. The present study assesses this conjecture.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"851-855"},"PeriodicalIF":3.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143565437","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}

{"title":"Potential Demand Forecasting for Steel Products in Spot Markets Using a Hybrid SARIMA-LSSVM Approach","authors":"Junting Huang,&nbsp;Ying Meng,&nbsp;Min Xiao,&nbsp;Chang Liu,&nbsp;Yun Dong","doi":"10.1002/for.3259","DOIUrl":"https://doi.org/10.1002/for.3259","url":null,"abstract":"<div>\u0000 \u0000 <p>Compared to make-to-order production based on customer order, make-to-stock based on forecast can effectively reduce inventory level and production cost. However, due to high randomness of spot markets and many uncertainties in production environments, it is hard to forecast the products accurately. In this article, a hybrid model combining seasonal autoregressive integrated moving average (SARIMA) and least square support vector machines (LSSVMs) is proposed to forecast the potential demand of steel products. First, the SARIMA based on a multiobjective differential evolution with improved mutation strategies is developed to extract linear components of the potential demand. Then, a sparse strategy is designed to extract useful data and hence reduce computation complexity without loss of accuracy. Next, the LSSVMs combined with a single-objective differential evolution are adopted to extract nonlinear components of the potential demand. Finally, the experimental results on a real-world instance demonstrate the effectiveness of the proposed model and algorithm.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 5","pages":"1623-1637"},"PeriodicalIF":3.4,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144525117","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}

{"title":"Forecasting the Confirmed COVID-19 Cases Using Modal Regression","authors":"Xin Jing,&nbsp;Jin Seo Cho","doi":"10.1002/for.3261","DOIUrl":"https://doi.org/10.1002/for.3261","url":null,"abstract":"<div>\u0000 \u0000 <p>This study utilizes modal regression to forecast the cumulative confirmed COVID-19 cases in Canada, Japan, South Korea, and the United States. The objective is to improve the accuracy of the forecasts compared to standard mean and median regressions. To evaluate the performance of the forecasts, we conduct simulations and introduce a metric called the coverage quantile function (CQF), which is optimized using modal regression. By applying modal regression to popular time-series models for COVID-19 data, we provide empirical evidence that the forecasts generated by the modal regression outperform those produced by the mean and median regressions in terms of the CQF. This finding addresses the limitations of the mean and median regression forecasts.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1578-1601"},"PeriodicalIF":3.4,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144207046","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}

{"title":"Trading VIX on Volatility Forecasts: Another Volatility Puzzle?","authors":"Stavros Degiannakis,&nbsp;Panagiotis Delis,&nbsp;George Filis,&nbsp;George Giannopoulos","doi":"10.1002/for.3257","DOIUrl":"https://doi.org/10.1002/for.3257","url":null,"abstract":"<div>\u0000 \u0000 <p>This study evaluates the economic usefulness of stock market implied volatility forecasts, based on their ability to improve the short-run trading decision-making process. The current literature aligns the forecast horizon with the frequency of the trading decision in order to evaluate different forecasting frameworks. By contrast, the premise of our paper is that these should not be necessarily related, but rather the evaluation should be based on the actual needs of the end-user. Thus, we evaluate whether the multiple days ahead stock market volatility forecasts vis-à-vis the 1-day ahead forecasts can improve the 1-day ahead trading profits from VIX and the S&amp;P500 futures. Our results suggest that indeed the 1-day ahead trading profits are significantly improved when the trading decisions are based on longer term volatility forecasts. More specifically, the highest trading gains are obtained when using the 22-day ahead forecasts. The results hold true for both VIX and S&amp;P500 futures day-ahead trading. Although there is no theoretical background regarding the fact that forecasting and trading horizons should not be aligned, we strongly motivate this potential issue, both from the statistical and financial points of view.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1602-1618"},"PeriodicalIF":3.4,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144207047","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}

{"title":"Common Shocks and Climate Risk in European Equities","authors":"Andrea Cipollini,&nbsp;Fabio Parla","doi":"10.1002/for.3256","DOIUrl":"https://doi.org/10.1002/for.3256","url":null,"abstract":"<div>\u0000 \u0000 <p>We examine the contribution of a shock to climate concern to the observed outperformance of a portfolio of European green stocks relative to a brown benchmark. We show, first, that an information set given by 1-month stock return and realized volatility of each stock constituent (and their cross-sectional averages) improves the (in-sample) forecasting performance for the return series relative to the traditional market risk factors proxied by Fama–French portfolios. Moreover, the identification of the shock to climate concern occurs in two stages: First, we compute the historical decomposition based on a Panel SVAR fitted to the return and volatility of each green and brown portfolio constituent. Then, the contribution of the first common shock to the historical decomposition of returns is purged of macroeconomic forecast errors, and the residual is interpreted as the innovation to climate concern. The empirical evidence is robust to a number of different selections of stocks entering the green and brown portfolio.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"1165-1192"},"PeriodicalIF":3.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564589","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}

{"title":"Forecasting Transition of Personal Travel Behavior in a Sharing Economy: Evidence From Consumer Preferences of Travel Modes","authors":"Stephen Youngjun Park,&nbsp;Hyunhong Choi,&nbsp;Yasemin Boztuğ,&nbsp;HyungBin Moon","doi":"10.1002/for.3255","DOIUrl":"https://doi.org/10.1002/for.3255","url":null,"abstract":"<div>\u0000 \u0000 <p>The impacts of new mobility services on the market have led changes in consumer's travel behavior but also to various conflicts with the traditional transportation modes. Gaining social consensus, deriving policy and market strategies suitable for the different transportation modes is crucial. This study's objective is to make predictions about future transportation markets by examining consumers' preferences and choices regarding transportation mode. Specifically, this study employs the mixed multiple discrete-continuous extreme value model to quantitatively identify consumers' attitudes towards various types of transportation modes. In addition to evaluating consumer preferences and usage choices of different transportation modes, the study examines the intricate relationship between transportation modes by using market simulations to forecast future transportation markets. The results show significant potential of shared mobility services in the transportation market and identify complementary effects between taxi and ride-sharing services. It is expected that policy implications derived can contribute to sustainably developing the transportation sector.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1563-1577"},"PeriodicalIF":3.4,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206537","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}

{"title":"Deciphering Long-Term Economic Growth: An Exploration With Leading Machine Learning Techniques","authors":"Zin Mar Oo,&nbsp;Ching-Yang Lin,&nbsp;Makoto Kakinaka","doi":"10.1002/for.3254","DOIUrl":"https://doi.org/10.1002/for.3254","url":null,"abstract":"<div>\u0000 \u0000 <p>Existing studies mainly focus on short-term economic forecasts, but research on long-term projections, particularly for periods spanning 6–10 years, remains insufficient, despite its importance. This gap may arise from the limitations of traditional linear methods in prediction tasks and pattern recognition, whereas machine learning techniques may help overcome these challenges. To address this, we employ five widely used machine learning models—artificial neural networks (ANN), random forest regression (RF), gradient boosting regression (GBR), extreme gradient boosting (XGBoost), and support vector regression (SVR)—using cross-country data from 109 countries between 1961 and 2019. To ensure robustness, we employ two distinct sampling methods for model validation. Our findings reveal that the ANN model outperforms others, particularly in long-term predictions (6–10 years), with an average out-of-sample prediction \u0000<span></span><math>\u0000 <mi>R</mi></math>-squared of 0.89. Furthermore, analyses using permutation feature importance (PFI) and SHapley Additive exPlanations (SHAP) methods indicate that while current growth rates are critical for short-term forecasts (1–3 years), two primary variables representing a country's foundational characteristics—real GDP per capita and “country-feature,” akin to a country dummy variable—are crucial for long-term predictions (4–10 years). This outcome demonstrates the ANN model's capacity to capture each country's unique characteristics and, through its highly non-linear nature, successfully execute complex, long-range forecasts. These results unveil the remarkable potential of machine learning in the realm of long-term economic forecasting.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1531-1562"},"PeriodicalIF":3.4,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206468","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}

{"title":"An Explainable ADASYN-Based Focal Loss Approach for Credit Assessment","authors":"Shaukat Ali Shahee,&nbsp;Rujavi Patel","doi":"10.1002/for.3252","DOIUrl":"https://doi.org/10.1002/for.3252","url":null,"abstract":"<div>\u0000 \u0000 <p>The integration of deep learning techniques with financial technology (fintech) has revolutionized the credit risk analysis, a critical component of financial risk management. A pervasive challenge in credit risk assessment lies in the skewed distribution of data, hindering accurate predictions, particularly for minority class instances. In available literature, various solutions have been proposed to address class imbalance, albeit with limitations. Focal loss is one of the well-known loss functions proposed for handling class imbalance by running the hyperparameter \u0000<span></span><math>\u0000 <mi>γ</mi></math>. However, imbalance still remains in terms of number of hard-to-learn observations between the classes. In this paper, we have proposed integration of ADASYN with focal loss to mitigate class imbalance and enhance credit scoring accuracy. ADASYN systematically generates synthetic data based on hard-to-learn examples to counter skewed distributions, while focal loss prioritizes the training of challenging examples, fostering a more balanced model performance. This approach has been rigorously tested using real-world imbalanced datasets and credit assessment data, and the outcomes have been compared against a range of sample technique and loss function combinations. The results clearly show that our suggested strategy is better than other approaches. Although improving the accuracy of credit risk analysis is critical, model interpretability is just as important for enabling financial analysts to make wise choices. In order to solve this, we have measured the global and local contributions of each feature using SHAP (Shapley additive explanation). According to global interpretability, the top 4 parameters influencing credit risk assessment are checking account status, loan purpose, borrower age, credit history, and interest rate/installment rate. Moreover, local interpretability analysis reveals quantitative and direction differences in feature contributions. These revelations not only broaden our knowledge of credit assessment services but also highlight how important a role they could play in attracting new clients and generating income. This paper also highlights how the suggested approach may be scaled to other imbalanced real-world datasets, demonstrating how it can improve model performance in terms of AUC, G-mean, and F-measure.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1513-1530"},"PeriodicalIF":3.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206948","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}