Pub Date : 2025-02-07DOI: 10.1016/j.ijforecast.2025.01.004
Robert Fildes, Fergus Bolger, Paul Goodwin, Nigel Harvey, Matthias Seifert
{"title":"Introduction to the Special Issue on Judgment in Forecasting","authors":"Robert Fildes, Fergus Bolger, Paul Goodwin, Nigel Harvey, Matthias Seifert","doi":"10.1016/j.ijforecast.2025.01.004","DOIUrl":"10.1016/j.ijforecast.2025.01.004","url":null,"abstract":"","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 419-423"},"PeriodicalIF":6.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-07DOI: 10.1016/j.ijforecast.2025.01.002
Christian Donner , Anuj Mishra , Hideaki Shimazaki
Learning and forecasting stochastic time series is essential in various scientific fields. However, despite the proposals of nonlinear filters and deep-learning methods, it remains challenging to capture nonlinear dynamics from a few noisy samples and predict future trajectories with uncertainty estimates while maintaining computational efficiency. Here, we propose a fast algorithm to learn and forecast nonlinear dynamics from noisy time series data. A key feature of the proposed model is kernel functions applied to projected lines, enabling the fast and efficient capture of nonlinearities in the latent dynamics. Through empirical case studies and benchmarking, the model demonstrates its effectiveness at learning and forecasting complex nonlinear dynamics, offering a valuable tool for researchers and practitioners in time series analysis.
{"title":"A projected nonlinear state-space model for forecasting time series signals","authors":"Christian Donner , Anuj Mishra , Hideaki Shimazaki","doi":"10.1016/j.ijforecast.2025.01.002","DOIUrl":"10.1016/j.ijforecast.2025.01.002","url":null,"abstract":"<div><div>Learning and forecasting stochastic time series is essential in various scientific fields. However, despite the proposals of nonlinear filters and deep-learning methods, it remains challenging to capture nonlinear dynamics from a few noisy samples and predict future trajectories with uncertainty estimates while maintaining computational efficiency. Here, we propose a fast algorithm to learn and forecast nonlinear dynamics from noisy time series data. A key feature of the proposed model is kernel functions applied to projected lines, enabling the fast and efficient capture of nonlinearities in the latent dynamics. Through empirical case studies and benchmarking, the model demonstrates its effectiveness at learning and forecasting complex nonlinear dynamics, offering a valuable tool for researchers and practitioners in time series analysis.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1296-1309"},"PeriodicalIF":6.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-03DOI: 10.1016/j.ijforecast.2024.12.003
Joris Depoortere, Johan Driesen, Johan Suykens, Hussain Syed Kazmi
Deep learning models have gained increasing prominence in recent years in solar photovoltaic (PV) forecasting. One drawback of these models is that they require a lot of high-quality data to perform well. This is often infeasible in practice, due to poor measurement infrastructure in legacy systems and the rapid build-up of new solar systems across the world. This paper proposes SolNet: a novel, general-purpose, multivariate solar power forecaster, which addresses these challenges by using a two-step forecasting pipeline that incorporates transfer learning from abundant synthetic data generated from PVGIS, before fine-tuning on observational data.
Using actual production data from hundreds of sites in The Netherlands, Australia, and Belgium, we show that SolNet improves forecasting performance over data-scarce settings as well as baseline models. We find transfer learning benefits to be the strongest when only limited observational data are available. At the same time, we provide several guidelines and considerations for transfer learning practitioners, as our results show that weather data, seasonal patterns, and possible misspecification in source location can have a major impact on the results. The SolNet models created in this way are applicable for any land-based solar photovoltaic system across the planet to obtain improved forecasting capabilities.
{"title":"SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe","authors":"Joris Depoortere, Johan Driesen, Johan Suykens, Hussain Syed Kazmi","doi":"10.1016/j.ijforecast.2024.12.003","DOIUrl":"10.1016/j.ijforecast.2024.12.003","url":null,"abstract":"<div><div>Deep learning models have gained increasing prominence in recent years in solar photovoltaic (PV) forecasting. One drawback of these models is that they require a lot of high-quality data to perform well. This is often infeasible in practice, due to poor measurement infrastructure in legacy systems and the rapid build-up of new solar systems across the world. This paper proposes SolNet: a novel, general-purpose, multivariate solar power forecaster, which addresses these challenges by using a two-step forecasting pipeline that incorporates transfer learning from abundant synthetic data generated from PVGIS, before fine-tuning on observational data.</div><div>Using actual production data from hundreds of sites in The Netherlands, Australia, and Belgium, we show that SolNet improves forecasting performance over data-scarce settings as well as baseline models. We find transfer learning benefits to be the strongest when only limited observational data are available. At the same time, we provide several guidelines and considerations for transfer learning practitioners, as our results show that weather data, seasonal patterns, and possible misspecification in source location can have a major impact on the results. The SolNet models created in this way are applicable for any land-based solar photovoltaic system across the planet to obtain improved forecasting capabilities.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1223-1236"},"PeriodicalIF":6.9,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1016/j.ijforecast.2024.12.005
E.J. Whitehouse , D.I. Harvey , S.J. Leybourne
Asset price bubbles and crashes can have severe consequences for the stability of financial and economic systems. Policymakers require timely identification of such bubbles in order to respond to their emergence. In this paper we propose new econometric procedures that improve the speed of detection for an emerging asset price bubble in real time. Our new monitoring procedures make use of alternative variance standardisations that are better able to capture the behaviour of the underlying process during a bubble phase. We derive asymptotic results to show that using these alternative variance standardisations does not increase the probability of false detection under the no-bubble (unit root) null hypothesis relative to existing procedures. However, Monte Carlo simulations demonstrate that much earlier detection becomes possible with our new procedures under the bubble (explosive autoregressive) alternative. Empirical applications to OECD housing markets and bitcoin prices show the value in terms of earlier detection of bubbles that our new procedures can achieve. In particular, we show that the United States housing bubble that preceded the global financial crisis could have been detected as early as 1999:Q1 by our new procedures.
{"title":"Real-time monitoring procedures for early detection of bubbles","authors":"E.J. Whitehouse , D.I. Harvey , S.J. Leybourne","doi":"10.1016/j.ijforecast.2024.12.005","DOIUrl":"10.1016/j.ijforecast.2024.12.005","url":null,"abstract":"<div><div>Asset price bubbles and crashes can have severe consequences for the stability of financial and economic systems. Policymakers require timely identification of such bubbles in order to respond to their emergence. In this paper we propose new econometric procedures that improve the speed of detection for an emerging asset price bubble in real time. Our new monitoring procedures make use of alternative variance standardisations that are better able to capture the behaviour of the underlying process during a bubble phase. We derive asymptotic results to show that using these alternative variance standardisations does not increase the probability of false detection under the no-bubble (unit root) null hypothesis relative to existing procedures. However, Monte Carlo simulations demonstrate that much earlier detection becomes possible with our new procedures under the bubble (explosive autoregressive) alternative. Empirical applications to OECD housing markets and bitcoin prices show the value in terms of earlier detection of bubbles that our new procedures can achieve. In particular, we show that the United States housing bubble that preceded the global financial crisis could have been detected as early as 1999:Q1 by our new procedures.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1260-1277"},"PeriodicalIF":6.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1016/j.ijforecast.2024.08.006
Philippe Goulet Coulombe
Time-varying parameter (TVP) models are frequently used in economics to capture structural change. I highlight a rather underutilized fact—that these are actually ridge regressions. Instantly, this makes computations, tuning, and implementation much easier than in the state-space paradigm. Among other things, solving the equivalent dual ridge problem is computationally very fast even in high dimensions, and the crucial ‘amount of time variation’ is tuned by cross-validation. Evolving volatility is dealt with using a two-step ridge regression. I consider extensions that incorporate sparsity (the algorithm selects which parameters vary and which do not) and reduced-rank restrictions (variation is tied to a factor model). To demonstrate the usefulness of the approach, I use it to study the evolution of monetary policy in Canada using large time-varying local projections and TVP-VARs with demanding lag lengths. The applications require the estimation of up to 4600 TVPs, a task within the reach of the new method.
{"title":"Time-varying parameters as ridge regressions","authors":"Philippe Goulet Coulombe","doi":"10.1016/j.ijforecast.2024.08.006","DOIUrl":"10.1016/j.ijforecast.2024.08.006","url":null,"abstract":"<div><div>Time-varying parameter (TVP) models are frequently used in economics to capture structural change. I highlight a rather underutilized fact—that these are actually ridge regressions. Instantly, this makes computations, tuning, and implementation much easier than in the state-space paradigm. Among other things, solving the equivalent dual ridge problem is computationally very fast even in high dimensions, and the crucial ‘amount of time variation’ is tuned by cross-validation. Evolving volatility is dealt with using a two-step ridge regression. I consider extensions that incorporate sparsity (the algorithm selects which parameters vary and which do not) and reduced-rank restrictions (variation is tied to a factor model). To demonstrate the usefulness of the approach, I use it to study the evolution of monetary policy in Canada using large time-varying local projections and TVP-VARs with demanding lag lengths. The applications require the estimation of up to 4600 TVPs, a task within the reach of the new method.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 982-1002"},"PeriodicalIF":6.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-24DOI: 10.1016/j.ijforecast.2024.12.008
Panagiotis Samartzis
We perform a comparative analysis of a wide array of approaches for the problem of forecasting the relative performance among different tradable assets in the framework of the M6 competition. To produce the forecasts, we employ various models spanning probabilistic, classification, and time-series methods, each approaching the problem from a different perspective. We demonstrate that in the case of financial forecasting, simple machine learning approaches have better performance compared to more complex deep-learning models. Furthermore, approaching the problem as a classification task appears to be beneficial. We also confirm findings from existing literature that using simple ensemble techniques can improve performance, and that forecasting performance is better for exchange-traded funds and assets that have lower idiosyncratic volatility. Finally, we benchmark our results against the performance of teams that participated in the M6 competition.
{"title":"Predicting the relative performance among financial assets: A comparative analysis of different approaches","authors":"Panagiotis Samartzis","doi":"10.1016/j.ijforecast.2024.12.008","DOIUrl":"10.1016/j.ijforecast.2024.12.008","url":null,"abstract":"<div><div>We perform a comparative analysis of a wide array of approaches for the problem of forecasting the relative performance<span> among different tradable assets in the framework of the M6 competition. To produce the forecasts, we employ various models spanning probabilistic, classification, and time-series methods, each approaching the problem from a different perspective. We demonstrate that in the case of financial forecasting, simple machine learning approaches<span> have better performance compared to more complex deep-learning models. Furthermore, approaching the problem as a classification task appears to be beneficial. We also confirm findings from existing literature that using simple ensemble techniques can improve performance, and that forecasting performance is better for exchange-traded funds and assets that have lower idiosyncratic volatility. Finally, we benchmark our results against the performance of teams that participated in the M6 competition.</span></span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1428-1449"},"PeriodicalIF":7.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-24DOI: 10.1016/j.ijforecast.2024.12.002
Rebekka Buse , Konstantin Görgen , Melanie Schienle
We study the prediction of value at risk (VaR) for cryptocurrencies. In contrast to classic assets, returns of cryptocurrencies are often highly volatile and characterized by large fluctuations around single events. Analyzing a comprehensive set of 105 major cryptocurrencies, we show that generalized random forests (GRF) adapted to quantile prediction have superior performance over other established methods such as quantile regression, GARCH-type models, and CAViaR models. This advantage is especially pronounced in unstable times and for classes of highly volatile cryptocurrencies. Furthermore, we identify important predictors during such times and show their influence on forecasting over time. Moreover, a comprehensive simulation study indicates that the GRF methodology is at least on par with existing methods in VaR predictions for standard types of financial returns, and clearly superior in the cryptocurrency setup.
{"title":"Predicting value at risk for cryptocurrencies with generalized random forests","authors":"Rebekka Buse , Konstantin Görgen , Melanie Schienle","doi":"10.1016/j.ijforecast.2024.12.002","DOIUrl":"10.1016/j.ijforecast.2024.12.002","url":null,"abstract":"<div><div>We study the prediction of value at risk (VaR) for cryptocurrencies. In contrast to classic assets, returns of cryptocurrencies are often highly volatile and characterized by large fluctuations around single events. Analyzing a comprehensive set of 105 major cryptocurrencies, we show that generalized random forests (GRF) adapted to quantile prediction have superior performance over other established methods such as quantile regression, GARCH-type models, and CAViaR models. This advantage is especially pronounced in unstable times and for classes of highly volatile cryptocurrencies. Furthermore, we identify important predictors during such times and show their influence on forecasting over time. Moreover, a comprehensive simulation study indicates that the GRF methodology is at least on par with existing methods in VaR predictions for standard types of financial returns, and clearly superior in the cryptocurrency setup.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1199-1222"},"PeriodicalIF":6.9,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-22DOI: 10.1016/j.ijforecast.2024.12.001
Vahid Karimi Motahhar , Thomas S. Gruca
Biases in human forecasters lead to poor calibration. We assess how formal training affects two types of bias in probabilistic forecasts of binary outcomes. Compensatory bias occurs when underestimation in one range of probabilities (e.g., less than 50%) is accompanied by overestimation in the opposite range. Non-compensatory bias occurs when the direction of misestimation is consistent throughout the entire range of probabilities. We present a new approach to modeling probabilistic forecasts to determine the extent and direction of compensatory and non-compensatory biases. Using data from the Good Judgment Project, we model the effects of training (randomly assigned) on the calibration of 39,481 initial forecasts from 851 forecasters across two years of the contest. The forecasts exhibit significant indications of both compensatory and non-compensatory biases across all forecasters. Training significantly reduces the compensatory bias in both years. It reduces the non-compensatory bias only in the second year of the contest.
{"title":"How does training improve individual forecasts? Modeling differences in compensatory and non-compensatory biases in geopolitical forecasts","authors":"Vahid Karimi Motahhar , Thomas S. Gruca","doi":"10.1016/j.ijforecast.2024.12.001","DOIUrl":"10.1016/j.ijforecast.2024.12.001","url":null,"abstract":"<div><div>Biases in human forecasters lead to poor calibration. We assess how formal training affects two types of bias in probabilistic forecasts of binary outcomes. Compensatory bias occurs when underestimation in one range of probabilities (e.g., less than 50%) is accompanied by overestimation in the opposite range. Non-compensatory bias occurs when the direction of misestimation is consistent throughout the entire range of probabilities. We present a new approach to modeling probabilistic forecasts to determine the extent and direction of compensatory and non-compensatory biases. Using data from the Good Judgment Project, we model the effects of training (randomly assigned) on the calibration of 39,481 initial forecasts from 851 forecasters across two years of the contest. The forecasts exhibit significant indications of both compensatory and non-compensatory biases across all forecasters. Training significantly reduces the compensatory bias in both years. It reduces the non-compensatory bias only in the second year of the contest.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 487-498"},"PeriodicalIF":6.9,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-22DOI: 10.1016/j.ijforecast.2024.12.006
Jose M.G. Vilar
This paper presents the winning method that achieved fifth place overall in the M6 financial forecasting competition. The method is based on the idea that, under the efficient market hypothesis, it is often more effective to predict values close to the expected averages of categories and trends than to try to make precise predictions. By leveraging low-variability prediction methods, we forecast both the relative performance of multiple assets and their optimal investment positions. We demonstrate that combining asset-class and temporal averages yields modest but consistent advantages over reference indices. The results highlight the challenges of achieving above-average returns in efficient markets and the potential benefits of low-variability prediction methods in such contexts.
{"title":"Quasi-average predictions and regression to the trend: An application to the M6 financial forecasting competition","authors":"Jose M.G. Vilar","doi":"10.1016/j.ijforecast.2024.12.006","DOIUrl":"10.1016/j.ijforecast.2024.12.006","url":null,"abstract":"<div><div>This paper presents the winning method that achieved fifth place overall in the M6 financial forecasting competition. The method is based on the idea that, under the efficient market hypothesis, it is often more effective to predict values close to the expected averages of categories and trends than to try to make precise predictions. By leveraging low-variability prediction methods, we forecast both the relative performance of multiple assets and their optimal investment positions. We demonstrate that combining asset-class and temporal averages yields modest but consistent advantages over reference indices. The results highlight the challenges of achieving above-average returns in efficient markets and the potential benefits of low-variability prediction methods in such contexts.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1505-1513"},"PeriodicalIF":7.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-21DOI: 10.1016/j.ijforecast.2024.12.007
Jianqiu Wang , Zhuo Wang , Ke Wu
We empirically investigate the relation between anomaly portfolio returns and market return predictability in the Chinese stock market. Using 132 long-leg, short-leg, and long-short anomaly portfolio returns, we employ various shrinkage-based statistical learning methods to capture predictive signals of the anomalies in a high-dimensional setting. Our analysis reveals statistically and economically significant return predictability using long- and short-leg anomaly portfolio returns. Moreover, high arbitrage risk enhances forecasting performance, supporting that the predictability stems from mispricing correction persistence. Contrary to findings in the US stock market, we find little evidence that the long-short anomaly portfolios contribute to market return predictability in China, due to the low persistence of asymmetric mispricing corrections. We provide simulation evidence to justify the distinct prediction patterns for the US and Chinese stock markets.
{"title":"Forecasting stock market return with anomalies: Evidence from China","authors":"Jianqiu Wang , Zhuo Wang , Ke Wu","doi":"10.1016/j.ijforecast.2024.12.007","DOIUrl":"10.1016/j.ijforecast.2024.12.007","url":null,"abstract":"<div><div>We empirically investigate the relation between anomaly portfolio returns and market return predictability in the Chinese stock market. Using 132 long-leg, short-leg, and long-short anomaly portfolio returns, we employ various shrinkage-based statistical learning methods to capture predictive signals of the anomalies in a high-dimensional setting. Our analysis reveals statistically and economically significant return predictability using long- and short-leg anomaly portfolio returns. Moreover, high arbitrage risk enhances forecasting performance, supporting that the predictability stems from mispricing correction persistence. Contrary to findings in the US stock market, we find little evidence that the long-short anomaly portfolios contribute to market return predictability in China, due to the low persistence of asymmetric mispricing corrections. We provide simulation evidence to justify the distinct prediction patterns for the US and Chinese stock markets.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1278-1295"},"PeriodicalIF":6.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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