Journal of Forecasting最新文献

{"title":"A Novel Hybrid Nonlinear Forecasting Model for Interval-Valued Gas Prices","authors":"Haowen Bao,&nbsp;Yongmiao Hong,&nbsp;Yuying Sun,&nbsp;Shouyang Wang","doi":"10.1002/for.3272","DOIUrl":"https://doi.org/10.1002/for.3272","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes a novel hybrid nonlinear interval decomposition ensemble (NIDE) framework to improve forecasting accuracy of interval-valued gas prices. The framework first decomposes the price series using bivariate empirical mode decomposition and interval multiscale permutation entropy to capture dynamics driven by long-term trends, events, and short-term fluctuations. Tailored models are then employed for each component, including a threshold autoregressive interval model, interval event study methodology, and interval random forest. Finally, an ensemble prediction integrates the component forecasts. Empirical results show that the NIDE approach significantly outperforms benchmarks in out-of-sample forecasting of interval-valued natural gas prices. For instance, the RMSE improvements range from 10.3% to 38.8% compared to benchmark models. Additionally, the NIDE approach not only enhances accuracy but also provides economic interpretation by identifying drivers like speculative trading and public interest proxied by online trends.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 5","pages":"1826-1848"},"PeriodicalIF":3.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524778","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":"Deep Learning Quantile Regression for Interval-Valued Data Prediction","authors":"Huiyuan Wang,&nbsp;Ruiyuan Cao","doi":"10.1002/for.3271","DOIUrl":"https://doi.org/10.1002/for.3271","url":null,"abstract":"<div>\u0000 \u0000 <p>Interval-valued data are a special symbolic data, which contains rich information. The prediction of interval-valued data is a challenging task. In terms of predicting interval-valued data, machine learning algorithms typically consider mean regression, which is sensitive to outliers and may lead to unreliable results. As an important complement to mean regression, in this paper, a quantile regression artificial neural network based on a center and radius method (QRANN-CR) is proposed to address this problem. Numerical studies have been conducted to evaluate the proposed method, comparing with several traditional models, including the interval-valued quantile regression, the center method, the MinMax method, and the bivariate center and radius method. The simulation results demonstrate that the proposed QRANN-CR model is an effective tool for predicting interval-valued data with higher accuracy and is more robust than the other methods. A real data analysis is provided to illustrate the application of QRANN-CR.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 5","pages":"1806-1825"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144525213","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 Two-Stage Training Method for Modeling Constrained Systems With Neural Networks","authors":"C. Coelho,&nbsp;M. Fernanda P. Costa,&nbsp;L.L. Ferrás","doi":"10.1002/for.3270","DOIUrl":"https://doi.org/10.1002/for.3270","url":null,"abstract":"<div>\u0000 \u0000 <p>Real-world systems are often formulated as constrained optimization problems. Techniques to incorporate constraints into neural networks (NN), such as neural ordinary differential equations (Neural ODEs), have been used. However, these introduce hyperparameters that require manual tuning through trial and error, raising doubts about the successful incorporation of constraints into the generated model. This paper describes in detail the two-stage training method for Neural ODEs, a simple, effective, and penalty parameter-free approach to model constrained systems. In this approach, the constrained optimization problem is rewritten as two optimization subproblems that are solved in two stages. The first stage aims at finding feasible NN parameters by minimizing a measure of constraints violation. The second stage aims to find the optimal NN parameters by minimizing the loss function while keeping inside the feasible region. We experimentally demonstrate that our method produces models that satisfy the constraints and also improves their predictive performance, thus ensuring compliance with critical system properties and also contributing to reducing data quantity requirements. Furthermore, we show that the proposed method improves the convergence to an optimal solution and improves the explainability of Neural ODE models. Our proposed two-stage training method can be used with any NN architectures.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 5","pages":"1785-1805"},"PeriodicalIF":3.4,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524991","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":"Stock Price Forecasting With Integration of Sectoral Behavior: A Deep Auto-Optimized Multimodal Framework","authors":"Renu Saraswat,&nbsp;Ajit Kumar","doi":"10.1002/for.3265","DOIUrl":"https://doi.org/10.1002/for.3265","url":null,"abstract":"<div>\u0000 \u0000 <p>This study proposes a novel deep auto-optimized architecture for stock price forecasting that integrates sectoral behavior with individual stock sentiment to improve predictive accuracy. Traditional stock prediction models often focus solely on individual stock behavior, overlooking the impact of broader sectoral trends. The proposed approach utilizes advanced deep learning models, including gated recurrent units (GRU), bidirectional GRU, long short-term memory (LSTM), and bidirectional LSTM, with their hybrid ensembles. These models are built using the Keras functional API and auto ML network architecture search technology. The current deep auto-optimized multimodal framework incorporates sectoral behavior, significantly improving performance metrics. This research highlights the critical role of integrating sectoral behavior in stock price prediction models.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 5","pages":"1767-1784"},"PeriodicalIF":3.4,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524540","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":"Correction to “Regime-Switching Density Forecasts Using Economists' Scenarios”","authors":"","doi":"10.1002/for.3273","DOIUrl":"https://doi.org/10.1002/for.3273","url":null,"abstract":"&lt;p&gt;\u0000 &lt;span&gt;Moramarco, G.&lt;/span&gt; (&lt;span&gt;2025&lt;/span&gt;), &lt;span&gt;Regime-Switching Density Forecasts Using Economists' Scenarios&lt;/span&gt;. &lt;i&gt;Journal of Forecasting&lt;/i&gt;, &lt;span&gt;44&lt;/span&gt;: &lt;span&gt;833&lt;/span&gt;–&lt;span&gt;845&lt;/span&gt;. https://doi.org/10.1002/for.3228.&lt;/p&gt;&lt;p&gt;In the third paragraph of Section 3.1 (“Priors and Fed Scenarios”), the sentence “Accordingly, the prior means for the regime-specific intercepts are set to \u0000&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;b&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;2.1&lt;/mn&gt;\u0000 &lt;mo&gt;/&lt;/mo&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;0.9&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;0.21&lt;/mn&gt;&lt;/math&gt; for the normal times regime, \u0000&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;b&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;2.125&lt;/mn&gt;\u0000 &lt;mo&gt;/&lt;/mo&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;0.9&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;0.2125&lt;/mn&gt;&lt;/math&gt; for the recession regime, and \u0000&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;b&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;6.275&lt;/mn&gt;\u0000 &lt;mo&gt;/&lt;/mo&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;0.9&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;0.6275&lt;/mn&gt;&lt;/math&gt; for the severe recession regime.” contained typographical errors in the formulas.&lt;/p&gt;&lt;p&gt;The correct text is: “Accordingly, the prior means for the regime-specific intercepts are set to \u0000&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;b&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;2.1&lt;/mn&gt;\u0000 &lt;mo&gt;·&lt;/mo&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;0.9&lt;/m","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 4","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206522","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":"Spread Option Pricing Method Based on Nonparametric Predictive Inference Copula","authors":"Ting He","doi":"10.1002/for.3262","DOIUrl":"https://doi.org/10.1002/for.3262","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper introduces a novel spread option pricing model, the nonparametric predictive inference–based copula spread option model (NPIC-SOM), designed to evaluate the interdependence of multiple underlying assets. Through empirical analysis focused on Brent-WTI spread options, a widely traded derivative, we compare the predictive performance of the NPIC-SOM against the traditional geometric Brownian motion crack spread option model (GBM-CSOM). Our findings reveal that the NPIC-SOM not only forecasts spread option prices closer to empirical values but also captures market fluctuations more accurately than the GBM-CSOM. This superiority extends across various option types, moneyness levels and delta hedge efficiency. Furthermore, the NPIC-SOM's reliance on time-varying parameters enhances prediction accuracy, particularly for extreme market scenarios. These results indicate the practicality and efficiency of the NPIC-SOM as a robust spread option pricing model, offering valuable insights for option pricing strategies in financial markets.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 5","pages":"1755-1766"},"PeriodicalIF":3.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524548","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":"Information Illusion: Different Amounts of Information and Stock Price Estimates","authors":"Andreas Oehler,&nbsp;Matthias Horn,&nbsp;Stefan Wendt","doi":"10.1002/for.3268","DOIUrl":"https://doi.org/10.1002/for.3268","url":null,"abstract":"<p>We initiate a questionnaire-based stock price forecast competition to analyze participants' perception of different amounts of information and the impact on stock price estimates. The results show that providing more information increases the perceived amount of relevant information but does not alter participants' stock price estimates and their accuracy. Individual participants' characteristics, such as gender, financial knowledge, or overconfidence, do not affect these findings. This means that the added information acts as placebic information and leads to information illusion. However, the added information has an impact on individual expectations about the stock price forecast competition itself and leads less overconfident investors to decrease their expectations regarding payoff and chances to win a prize. Our findings provide implications for practitioners and researchers alike. Both regulators and policy makers should consider that placebic information can significantly impact investors' perception, and, therefore, regulation on information that is provided to retail investors should focus on relevant and avoid irrelevant information. Researchers should be aware that placebic information asymmetrically influences expectations of participants in experiments who show different levels of overconfidence.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 5","pages":"1734-1754"},"PeriodicalIF":3.4,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524532","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":"Fire Prediction and Risk Identification With Interpretable Machine Learning","authors":"Shan Dai,&nbsp;Jiayu Zhang,&nbsp;Zhelin Huang,&nbsp;Shipei Zeng","doi":"10.1002/for.3266","DOIUrl":"https://doi.org/10.1002/for.3266","url":null,"abstract":"<div>\u0000 \u0000 <p>Fire safety is a primary concern in safeguarding lives and property. However, it is challenging to predict fire incidents and identify potential influencing factors due to limitations of data, model accuracy and interpretability. This paper proposes a novel scheme designed to enhance predictive and explainable capabilities by integrating multi-source data, adaptive machine learning methods, and Shapley additive explanation (SHAP) tools for more effective and applicable fire safety management. The scheme shows satisfactory prediction results by leveraging the data from grid-style management systems and our proposed machine learning method with dynamic time warping distance-based time series clustering, significantly outperforming the methods merely based on time series modeling. Moreover, clustered features help to clarify the main influencing risk factors and provide clearer insights for model interpretability. With global SHAP, community clusters capturing community fire event frequency, as well as historical records on fire police rescue, smoke alarms, and fire alarms, are found to be significant risk factors among all the features over the whole communities and periods via the model interpretability analysis, implying that communities where fires used to occur frequently are more likely to occur in future, which should be highly vigilant in real fire management. With local SHAP, specific risk factors that vary across communities can be identified for any single community with a given period. We demonstrate the potential of this integrated machine learning scheme in improving the prediction accuracy and risk identification applicability of fire incidents, which contributes to more effective and customized fire safety management.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 5","pages":"1699-1715"},"PeriodicalIF":3.4,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144525061","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":"Localized Global Time Series Forecasting Models Using Evolutionary Neighbor-Aided Deep Clustering Method","authors":"Hossein Abbasimehr,&nbsp;Ali Noshad","doi":"10.1002/for.3263","DOIUrl":"https://doi.org/10.1002/for.3263","url":null,"abstract":"<div>\u0000 \u0000 <p>Global forecasting models (GFMs) have become essential in time series prediction, as they enable cross-learning across multiple series. Although GFMs have consistently outperformed univariate approaches, their performance decreases when applied to heterogeneous time series datasets, such as those found in economic and financial applications. Clustering techniques have been used to create homogeneous time series clusters. However, the main limitations of current clustering-based GFMs are as follows: (1) employing handcrafted features instead of deep learning and (2) there is no guarantee that the resulting clusters are optimal in terms of prediction accuracy. To address these limitations, we propose a novel deep time series clustering model that jointly optimizes clustering and forecasting accuracy. The proposed method simultaneously optimizes the reconstruction, clustering, and prediction losses to ensure clusters are optimized for accurate forecasting. In addition, it employs a neighbor-aided autoencoder to capture cluster-oriented representations, leveraging neighboring time series to improve feature learning. Furthermore, we incorporate an evolutionary learning component, which iteratively refines clusters through crossover and mutation to find optimal clusters in terms of forecasting accuracy. We evaluate our proposed method on eight publicly available datasets considering various state-of-the-art forecasting benchmarks. Results indicate that across all datasets with 2620 time series, the proposed method obtains the lowest mean symmetric mean absolute percentage error (sMAPE) of 14.90, surpassing the baseline deep clustering (15.15). It exhibits enhancements of 1.28, 0.70, and 2.29 in mean sMAPE relative to DeepAR, N-BEATS, and transformer, respectively. Furthermore, it demonstrates improvements when compared to the existing clustering-based global models. The source code of the proposed clustering method is made publicly available at https://github.com/alinowshad/Evolutionary-Neighbor-Aided-Deep-Clustering-DEEPEN.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 5","pages":"1716-1733"},"PeriodicalIF":3.4,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144525062","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":"Deep Learning and Machine Learning Insights Into the Global Economic Drivers of the Bitcoin Price","authors":"Nezir Köse,&nbsp;Yunus Emre Gür,&nbsp;Emre Ünal","doi":"10.1002/for.3258","DOIUrl":"https://doi.org/10.1002/for.3258","url":null,"abstract":"<p>This study examines the connection between Bitcoin and global factors, including the VIX, the oil price, the US dollar index, the gold price, and interest rates estimated using the Federal funds rate and treasury securities rate, for forecasting analysis. Deep learning methodologies, including LSTM, GRU, CNN, and TFT, with machine learning algorithms such as XGBoost, LightGBM, and SVR, were employed to identify the optimal prediction model for the Bitcoin price. The findings indicate that the TFT model is the most successful predictive approach, with the gold price identified as the most relevant component in determining the Bitcoin price. After the gold indicator, the US dollar index was a substantial factor in the explanation of the Bitcoin price. The TFT model also included regulatory decisions and global events. It was estimated that the Bitcoin price was significantly influenced by the COVID-19 pandemic. After that, global climate events and China mining ban strongly affected the Bitcoin price. These findings indicate that regulatory decisions and global events determine the Bitcoin price in addition to macroeconomic factors. The VAR analysis was employed as a robustness check. The results indicate that gold and oil prices have a strong negative influence on Bitcoin, particularly in the long term. The paper has significant policy implications for investors, portfolio managers, and scholars.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 5","pages":"1666-1698"},"PeriodicalIF":3.4,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3258","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144525197","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}