Journal of Forecasting最新文献

{"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}

{"title":"Bank Capital Requirements, Lending Supply, and Economic Activity: A Scenario Analysis Perspective","authors":"Antonio M. Conti,&nbsp;Andrea Nobili,&nbsp;Federico M. Signoretti","doi":"10.1002/for.3239","DOIUrl":"https://doi.org/10.1002/for.3239","url":null,"abstract":"<div>\u0000 \u0000 <p>We evaluate the relation between bank capital, lending supply, and economic activity using Italian data over 1993–2015, a period which covers three key post-crisis regulatory and supervisory measures (the Basel III reform, the 2011 European Banking Authority [EBA] stress test, the European Central Bank's [ECB] Comprehensive Assessment, and launch of the Single Supervisory Mechanism—SSM). We quantify the impact of increased bank capital requirements using a novel procedure that recovers the magnitude of the policy measures, relying on scenario analysis and Bayesian VARs with a rich characterization of the banking sector. We document that the EBA and SSM measures unpredictably raised Tier 1 ratio by about 2.5 percentage points, leading to an average reduction in credit to firms and households by 5% and 4%, respectively, and to a decline in real GDP by over 2% and 4%. The Basel III bank capital increase is instead correctly anticipated in out-of-sample forecasting. These findings are robust to time-varying model parameters and consistent with narrative sign restriction techniques.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"1132-1164"},"PeriodicalIF":3.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564879","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":"Analyzing and Forecasting Container Throughput With a Hybrid Decomposition-Reconstruction-Ensemble Method: A Study of Two China Ports","authors":"Yi Xiao,&nbsp;Sheng Wu,&nbsp;Chen He,&nbsp;Yi Hu","doi":"10.1002/for.3253","DOIUrl":"https://doi.org/10.1002/for.3253","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate container throughput forecasting is critical for enhancing port efficiency and ensuring global trade stability, particularly in the face of economic uncertainties, geopolitical tensions, and supply chain disruptions. Existing forecasting methods often struggle to model the nonlinear, nonstationary, and noise-laden characteristics of throughput data, creating a clear gap in the ability to provide reliable predictions. To address this, we propose a novel hybrid model, VMD-ISE-TCNT, designed to tackle these challenges. The model employs variational mode decomposition (VMD) to decompose time series into intrinsic modes, with an improved signal energy (ISE) criterion automating the selection of optimal mode numbers. These modes are categorized into low- and high-frequency components and forecasted separately using temporal convolutional networks (TCNs), leveraging their strength in capturing multiscale temporal dependencies. The Theil UII-S loss function is integrated to enhance model robustness by prioritizing proportional accuracy and reducing outlier sensitivity. Empirical evaluations using 24 years of data from China's two largest container ports—Shanghai and Shenzhen—demonstrate the superior performance of the VMD-ISE-TCNT model compared to traditional and hybrid benchmarks. By addressing frequency-specific patterns and automating key processes, this model provides a scalable and interpretable solution for advancing port operations and ensuring resilience in global trade.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1424-1440"},"PeriodicalIF":3.4,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206794","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":"Economic Conditions and Predictability of US Stock Returns Volatility: Local Factor Versus National Factor in a GARCH-MIDAS Model","authors":"Afees A. S alisu,&nbsp;Wenting Liao,&nbsp;Rangan Gupta,&nbsp;Oguzhan Cepni","doi":"10.1002/for.3251","DOIUrl":"https://doi.org/10.1002/for.3251","url":null,"abstract":"<div>\u0000 \u0000 <p>The aim of this paper is to utilize the generalized autoregressive conditional heteroscedasticity–mixed data sampling (GARCH-MIDAS) framework to predict the daily volatility of state-level stock returns in the United States (US), based on the weekly metrics from the corresponding broad economic conditions indexes (ECIs). In light of the importance of a common factor in explaining a large proportion of the total variability in the state-level economic conditions, we first apply a dynamic factor model with stochastic volatility (DFM-SV) to filter out the national factor from the local components of weekly state-level ECIs. We find that both the local and national factors of the ECI generally tend to affect state-level volatility negatively. Furthermore, the GARCH-MIDAS model, supplemented by these predictors, surpasses the benchmark GARCH-MIDAS model with realized volatility (GARCH-MIDAS-RV) in a majority of states. Interestingly, the local factor often assumes a more influential role overall, compared with the national factor. Moreover, when the stochastic volatilities associated with the local and national factors are integrated into the GARCH-MIDAS model, they outperform the GARCH-MIDAS-RV in over 80% of the states. Our findings have important implications for investors and policymakers.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1441-1466"},"PeriodicalIF":3.4,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206795","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":"Estimation of Constrained Factor Models for High-Dimensional Time Series","authors":"Yitian Liu,&nbsp;Jiazhu Pan,&nbsp;Qiang Xia","doi":"10.1002/for.3249","DOIUrl":"https://doi.org/10.1002/for.3249","url":null,"abstract":"<div>\u0000 \u0000 <p>This article studies the estimation of the constrained factor models for high-dimensional time series. The approach is based on the eigenanalysis of a nonnegative definite matrix constructed from the autocovariance matrices. The convergence rate of the estimator for loading matrix and the asymptotic normality of the estimated factor score are explored under regularity conditions set for the proposed model. Our estimation for the constrained factor models can achieve the optimal rate of convergence even in the case of weak factors. The finite sample performance of our approach is examined and compared with the existing methods by Monte Carlo simulations. Our methodology is illustrated and supported by a real data example.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1467-1477"},"PeriodicalIF":3.4,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206796","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 Natural Gas Futures Prices Using Hybrid Machine Learning Models During Turbulent Market Conditions: The Case of the Russian–Ukraine Crisis","authors":"Pavan Kumar Nagula,&nbsp;Christos Alexakis","doi":"10.1002/for.3250","DOIUrl":"https://doi.org/10.1002/for.3250","url":null,"abstract":"<div>\u0000 \u0000 <p>Recently, many researchers have shown keen interest in natural gas price prediction using machine learning and hybrid architectures. Our research forecasts natural gas future prices with different hybrid machine learning models using over a hundred technical indicators. The hybrid deep cross-network model outperformed the single-stage deep cross-network regression and hybrid support vector machine models with 33% and 46% lower mean absolute error and 22% and 1.2 times better directional hit rate during 11 months of turbulent market circumstances due to the Russia–Ukraine crisis. The hybrid deep cross-network model is 14, 5, and 6 times more profitable than the hybrid support vector machine, the benchmark passive buy-and-hold strategy, and the single-stage deep cross-network regression models. The hybrid deep cross-network model is resilient during low- and high-volatility periods. Deep cross-network algorithm technical indicator interactions are more statistically significant than support vector machine polynomial kernel interactions. Energy traders and policymakers can exploit our findings.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1501-1512"},"PeriodicalIF":3.4,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206685","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 Gold Volatility in an Uncertain Environment: The Roles of Large and Small Shock Sizes","authors":"Li Zhang,&nbsp;Lu Wang,&nbsp;Yu Ji,&nbsp;Zhigang Pan","doi":"10.1002/for.3247","DOIUrl":"https://doi.org/10.1002/for.3247","url":null,"abstract":"<div>\u0000 \u0000 <p>In a complex and volatile macroeconomic environment, precious metals, which have the functions of preservation, appreciation, and hedging, play an important role in investment risk management. Therefore, this study adopts the extended GARCH-MIDAS model to investigate the underlying connection between gold price volatility and different uncertain shocks. In this paper, we consider five uncertainty indicators and then decompose them into different states to capture their shock sizes. Next, we introduce uncertainty shocks into the MIDAS structure to test whether they contain relevant and valid information about gold price volatility forecasts. Specifically, parameter significance suggests a positive association between uncertain indicators and gold price volatility, but variability in the influence of their shock sizes on gold price volatility. Out-of-sample results present that the extended model that includes asymmetric shock sizes outperforms other competitive models. Besides, the model that includes large shock sizes exhibits better predictive performance than the model that includes small shocks. Finally, based on the empirical analyses, this paper provides new insights for the gold industry, futures exchanges, government regulators, and investors engaged in futures hedging to achieve risk control and financial stability in response to uncertain shocks.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":"1478-1500"},"PeriodicalIF":3.4,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206684","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":"A Snapshot of Central Bank (Two-Year) Forecasting: A Mixed Picture","authors":"Charles A. E. Goodhart,&nbsp;Manoj Pradhan","doi":"10.1002/for.3244","DOIUrl":"https://doi.org/10.1002/for.3244","url":null,"abstract":"<div>\u0000 \u0000 <p>Between 2001 and 2023, Central Bank forecasts were patently inaccurate. In this paper, we argue that many of such forecast failings were already present during the earlier years of inflation targetry. Central Banks normally adjust monetary policy so that inflation hits the Inflation Target (IT) within two years. Since a central bank must believe its policy stance is appropriate to achieve this goal, its inflation forecast at the two-year horizon should generally be close to target. We examine whether this has held for three main Central Banks, Bank of England, ECB, and Fed. Although over the IT period prior to 2020, both forecasts and outcomes were commendably close to target, we found that this was due to a sizeable forecast <i>underestimate</i> of the effects of policy and inherent resilience to revive inflation after the GFC crisis hit, largely offset by an <i>overestimate</i> of the effect of monetary policy to restore inflation to target during the more normal times. We attribute such latter overestimation to an unwarranted belief in forward-looking, “well anchored”, expectations amongst households and firms, and to a failure to recognize the underlying disinflationary trends, especially in 2010–2019. We outline a novel means for assessing whether these latter trends were primarily demand driven, e.g. secular stagnation, or supply shocks, a labor supply surge. Finally, we examine how forecasts for the uncertainty of outcomes and relative risk (skew) to the central forecast have developed by examining the Bank of England's fan chart, again at the two-year horizon.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"1097-1131"},"PeriodicalIF":3.4,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143565297","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}