Pub Date : 2023-11-18DOI: 10.1016/j.ijforecast.2023.10.002
Jack Fosten, Shaoni Nandi
This paper proposes panel nowcasting methods to obtain timely predictions of CO2 emissions and energy consumption growth across all U.S. states. This is crucial, not least because of the increasing role of sub-national carbon abatement policies but also due to the very delayed publication of the data. Since the state-level CO2 data are constructed from energy consumption data, we propose a new panel bridge equation method. We use a mixed frequency set-up where economic data are first used to predict energy consumption growth. This is then used to predict CO2 emissions growth while allowing for cross-sectional dependence across states using estimated factors. We evaluate the models’ performance using an out-of-sample forecasting study. We find that nowcasts improve when incorporating timely data like electricity consumption relative to a simple benchmark. These gains are sizeable in many states, even around two years before the data are eventually released. In predicting CO2 emissions growth, nowcast accuracy gains are also notable well before the data release, especially after the current year’s energy consumption data are used in making the prediction.
{"title":"Nowcasting U.S. state-level CO2 emissions and energy consumption","authors":"Jack Fosten, Shaoni Nandi","doi":"10.1016/j.ijforecast.2023.10.002","DOIUrl":"https://doi.org/10.1016/j.ijforecast.2023.10.002","url":null,"abstract":"<p>This paper proposes panel nowcasting methods to obtain timely predictions of CO<sub>2</sub> emissions and energy consumption growth across all U.S. states. This is crucial, not least because of the increasing role of sub-national carbon abatement policies but also due to the very delayed publication of the data. Since the state-level CO<sub>2</sub> data are constructed from energy consumption data, we propose a new panel bridge equation method. We use a mixed frequency set-up where economic data are first used to predict energy consumption growth. This is then used to predict CO<sub>2</sub> emissions growth while allowing for cross-sectional dependence across states using estimated factors. We evaluate the models’ performance using an out-of-sample forecasting study. We find that nowcasts improve when incorporating timely data like electricity consumption relative to a simple benchmark. These gains are sizeable in many states, even around two years before the data are eventually released. In predicting CO<sub>2</sub> emissions growth, nowcast accuracy gains are also notable well before the data release, especially after the current year’s energy consumption data are used in making the prediction.</p>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138539107","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}
The Elo rating system is a simple and widely used method for calculating players’ skills from paired comparison data. Many have extended it in various ways. Yet the question of updating players’ variances remains to be further explored. In this paper, we address the issue of variance update by using the Laplace approximation for posterior distributions, together with a random walk model for the dynamics of players’ strengths and a lower bound on player variance. The random walk model is motivated by the Glicko system, but here we assume nonidentically distributed increments to deal with player heterogeneity. Experiments on men’s professional matches showed that the prediction accuracy slightly improves when the variance update is performed. They also showed that new players’ strengths may be better captured with the variance update.
Elo 评分系统是一种通过配对比较数据计算球员技能的简单而广泛使用的方法。许多人以各种方式对其进行了扩展。然而,更新球员方差的问题仍有待进一步探讨。在本文中,我们通过使用后验分布的拉普拉斯近似法、球员实力动态的随机漫步模型以及球员方差的下限来解决方差更新问题。随机行走模型是受格里科系统的启发,但在这里我们假设增量是非同分布的,以应对球员的异质性。对男子职业比赛的实验表明,进行方差更新后,预测准确率会略有提高。实验还表明,通过方差更新可以更好地捕捉新球员的实力。
{"title":"Rating players by Laplace’s approximation and dynamic modeling","authors":"Hsuan-Fu Hua, Ching-Ju Chang, Tse-Ching Lin, Ruby Chiu-Hsing Weng","doi":"10.1016/j.ijforecast.2023.10.004","DOIUrl":"10.1016/j.ijforecast.2023.10.004","url":null,"abstract":"<div><p>The Elo rating system is a simple and widely used method for calculating players’ skills from paired comparison data. Many have extended it in various ways. Yet the question of updating players’ variances remains to be further explored. In this paper, we address the issue of variance update by using the Laplace approximation for posterior distributions, together with a random walk model for the dynamics of players’ strengths and a lower bound on player variance. The random walk model is motivated by the Glicko system, but here we assume nonidentically distributed increments to deal with player heterogeneity. Experiments on men’s professional matches showed that the prediction accuracy slightly improves when the variance update is performed. They also showed that new players’ strengths may be better captured with the variance update.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135614914","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 : 2023-11-10DOI: 10.1016/j.ijforecast.2023.10.007
Weidong Lin , Abderrahim Taamouti
The Sharpe-ratio-maximizing portfolio becomes questionable under non-Gaussian returns, and it rules out, by construction, systemic risk, which can negatively affect its out-of-sample performance. In the present work, we develop a new performance ratio that simultaneously addresses these two problems when building optimal portfolios. To robustify the portfolio optimization and better represent extreme market scenarios, we simulate a large number of returns via a Monte Carlo method. This is done by obtaining probabilistic return forecasts through a distributional machine learning approach in a big data setting and then combining them with a fitted copula to generate return scenarios. Based on a large-scale comparative analysis conducted on the US market, the backtesting results demonstrate the superiority of our proposed portfolio selection approach against several popular benchmark strategies in terms of both profitability and minimizing systemic risk. This outperformance is robust to the inclusion of transaction costs.
{"title":"Portfolio selection under non-gaussianity and systemic risk: A machine learning based forecasting approach","authors":"Weidong Lin , Abderrahim Taamouti","doi":"10.1016/j.ijforecast.2023.10.007","DOIUrl":"10.1016/j.ijforecast.2023.10.007","url":null,"abstract":"<div><p><span>The Sharpe-ratio-maximizing portfolio becomes questionable under non-Gaussian returns, and it rules out, by construction, systemic risk, which can negatively affect its out-of-sample performance. In the present work, we develop a new performance ratio that simultaneously addresses these two problems when building optimal portfolios. To robustify the portfolio optimization and better represent extreme market scenarios, we simulate a large number of returns via a Monte Carlo method. This is done by obtaining probabilistic return forecasts through a distributional machine learning approach in a big data setting and then combining them with a fitted </span>copula to generate return scenarios. Based on a large-scale comparative analysis conducted on the US market, the backtesting results demonstrate the superiority of our proposed portfolio selection approach against several popular benchmark strategies in terms of both profitability and minimizing systemic risk. This outperformance is robust to the inclusion of transaction costs.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135615429","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 : 2023-11-07DOI: 10.1016/j.ijforecast.2023.11.001
Fred Collopy, Robert Fildes
{"title":"Obituary: J. Scott Armstrong","authors":"Fred Collopy, Robert Fildes","doi":"10.1016/j.ijforecast.2023.11.001","DOIUrl":"10.1016/j.ijforecast.2023.11.001","url":null,"abstract":"","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023001103/pdfft?md5=8ad9be59c8dbc64985e542ac6593e224&pid=1-s2.0-S0169207023001103-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135515744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-07DOI: 10.1016/j.ijforecast.2023.10.003
Daniele Girolimetto , George Athanasopoulos , Tommaso Di Fonzo , Rob J. Hyndman
Forecast reconciliation is a post-forecasting process that involves transforming a set of incoherent forecasts into coherent forecasts which satisfy a given set of linear constraints for a multivariate time series. In this paper, we extend the current state-of-the-art cross-sectional probabilistic forecast reconciliation approach to encompass a cross-temporal framework, where temporal constraints are also applied. Our proposed methodology employs both parametric Gaussian and non-parametric bootstrap approaches to draw samples from an incoherent cross-temporal distribution. To improve the estimation of the forecast error covariance matrix, we propose using multi-step residuals, especially in the time dimension where the usual one-step residuals fail. To address high-dimensionality issues, we present four alternatives for the covariance matrix, where we exploit the two-fold nature (cross-sectional and temporal) of the cross-temporal structure, and introduce the idea of overlapping residuals. We assess the effectiveness of the proposed cross-temporal reconciliation approaches through a simulation study that investigates their theoretical and empirical properties and two forecasting experiments, using the Australian GDP and the Australian Tourism Demand datasets. For both applications, the optimal cross-temporal reconciliation approaches significantly outperform the incoherent base forecasts in terms of the continuous ranked probability score and the energy score. Overall, the results highlight the potential of the proposed methods to improve the accuracy of probabilistic forecasts and to address the challenge of integrating disparate scenarios while coherently taking into account short-term operational, medium-term tactical, and long-term strategic planning.
{"title":"Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues","authors":"Daniele Girolimetto , George Athanasopoulos , Tommaso Di Fonzo , Rob J. Hyndman","doi":"10.1016/j.ijforecast.2023.10.003","DOIUrl":"10.1016/j.ijforecast.2023.10.003","url":null,"abstract":"<div><p>Forecast reconciliation is a post-forecasting process that involves transforming a set of incoherent forecasts into coherent forecasts which satisfy a given set of linear constraints for a multivariate time series. In this paper, we extend the current state-of-the-art cross-sectional probabilistic forecast reconciliation approach to encompass a cross-temporal framework, where temporal constraints are also applied. Our proposed methodology employs both parametric Gaussian and non-parametric bootstrap approaches to draw samples from an incoherent cross-temporal distribution. To improve the estimation of the forecast error covariance matrix, we propose using multi-step residuals, especially in the time dimension where the usual one-step residuals fail. To address high-dimensionality issues, we present four alternatives for the covariance matrix, where we exploit the two-fold nature (cross-sectional and temporal) of the cross-temporal structure, and introduce the idea of overlapping residuals. We assess the effectiveness of the proposed cross-temporal reconciliation approaches through a simulation study that investigates their theoretical and empirical properties and two forecasting experiments, using the Australian GDP and the Australian Tourism Demand datasets. For both applications, the optimal cross-temporal reconciliation approaches significantly outperform the incoherent base forecasts in terms of the continuous ranked probability score and the energy score. Overall, the results highlight the potential of the proposed methods to improve the accuracy of probabilistic forecasts and to address the challenge of integrating disparate scenarios while coherently taking into account short-term operational, medium-term tactical, and long-term strategic planning.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023001024/pdfft?md5=b9978f6e8d4d5d5d9fff37d9d9c92f92&pid=1-s2.0-S0169207023001024-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135509503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-04DOI: 10.1016/j.ijforecast.2023.09.007
Timo Dimitriadis , Tilmann Gneiting , Alexander I. Jordan , Peter Vogel
Probability forecasts for binary outcomes, often referred to as probabilistic classifiers or confidence scores, are ubiquitous in science and society, and methods for evaluating and comparing them are in great demand. We propose and study a triptych of diagnostic graphics focusing on distinct and complementary aspects of forecast performance: Reliability curves address calibration, receiver operating characteristic (ROC) curves diagnose discrimination ability, and Murphy curves visualize overall predictive performance and value. A Murphy curve shows a forecast’s mean elementary scores, including the widely used misclassification rate, and the area under a Murphy curve equals the mean Brier score. For a calibrated forecast, the reliability curve lies on the diagonal, and for competing calibrated forecasts, the ROC and Murphy curves share the same number of crossing points. We invoke the recently developed CORP (Consistent, Optimally binned, Reproducible, and Pool-Adjacent-Violators (PAV) algorithm-based) approach to craft reliability curves and decompose a mean score into miscalibration (MCB), discrimination (DSC), and uncertainty (UNC) components. Plots of the measure of discrimination ability versus the calibration metric visualize classifier performance across multiple competitors. The proposed tools are illustrated in empirical examples from astrophysics, economics, and social science.
{"title":"Evaluating probabilistic classifiers: The triptych","authors":"Timo Dimitriadis , Tilmann Gneiting , Alexander I. Jordan , Peter Vogel","doi":"10.1016/j.ijforecast.2023.09.007","DOIUrl":"10.1016/j.ijforecast.2023.09.007","url":null,"abstract":"<div><p>Probability forecasts for binary outcomes, often referred to as probabilistic classifiers or confidence scores, are ubiquitous in science and society, and methods for evaluating and comparing them are in great demand. We propose and study a triptych of diagnostic graphics focusing on distinct and complementary aspects of forecast performance: Reliability curves address calibration, receiver operating characteristic (ROC) curves diagnose discrimination ability, and Murphy curves visualize overall predictive performance and value. A Murphy curve shows a forecast’s mean elementary scores, including the widely used misclassification rate, and the area under a Murphy curve equals the mean Brier score. For a calibrated forecast, the reliability curve lies on the diagonal, and for competing calibrated forecasts, the ROC and Murphy curves share the same number of crossing points. We invoke the recently developed CORP (Consistent, Optimally binned, Reproducible, and Pool-Adjacent-Violators (PAV) algorithm-based) approach to craft reliability curves and decompose a mean score into miscalibration (MCB), discrimination (DSC), and uncertainty (UNC) components. Plots of the <span><math><mtext>DSC</mtext></math></span> measure of discrimination ability versus the calibration metric <span><math><mtext>MCB</mtext></math></span> visualize classifier performance across multiple competitors. The proposed tools are illustrated in empirical examples from astrophysics, economics, and social science.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023000997/pdfft?md5=bd26faa9dd0165399770a39be8802f6a&pid=1-s2.0-S0169207023000997-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-03DOI: 10.1016/j.ijforecast.2023.10.008
Wuyi Ye, Jinting Yang, Pengzhan Chen
Predicting price movements over a short period is a challenging problem in high-frequency trading. Deep learning methods have recently been used to forecast short-term prices via limit order book (LOB) data. In this paper, we propose a framework to convert LOB data into a series of standard images in 2D matrices and predict the mid-price movements via an image-based convolutional neural network (CNN). The empirical study shows that the image-based CNN model outperforms other traditional machine learning and deep learning methods based on raw LOB data. Our findings suggest that the additional information implicit in LOB images contributes to short-term price forecasting.
{"title":"Short-term stock price trend prediction with imaging high frequency limit order book data","authors":"Wuyi Ye, Jinting Yang, Pengzhan Chen","doi":"10.1016/j.ijforecast.2023.10.008","DOIUrl":"10.1016/j.ijforecast.2023.10.008","url":null,"abstract":"<div><p>Predicting price movements over a short period is a challenging problem in high-frequency trading. Deep learning methods have recently been used to forecast short-term prices via limit order book (LOB) data. In this paper, we propose a framework to convert LOB data into a series of standard images in 2D matrices and predict the mid-price movements via an image-based convolutional neural network (CNN). The empirical study shows that the image-based CNN model outperforms other traditional machine learning and deep learning methods based on raw LOB data. Our findings suggest that the additional information implicit in LOB images contributes to short-term price forecasting.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135410354","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 : 2023-10-30DOI: 10.1016/j.ijforecast.2023.10.001
Xixi Li, Jingsong Yuan
This paper proposes a new approach called DeepTVAR that employs a deep learning methodology for vector autoregressive (VAR) modeling and prediction with time-varying parameters. By optimizing the VAR parameters with a long short-term memory (LSTM) network, we retain the Markovian dependence for prediction purposes and make full use of the recurrent structure and powerful learning ability of the LSTM. To ensure the stability of the model, we enforce the causality condition on the autoregressive coefficients using the Ansley–Kohn transform. We provide a simulation study of the estimation ability using realistic curves generated from data. The model is extended to integrated VAR with time-varying parameters, and we compare its forecasting performance with existing methods when applied to energy price data.
本文提出了一种名为 DeepTVAR 的新方法,该方法采用深度学习方法对具有时变参数的向量自回归(VAR)进行建模和预测。通过用长短期记忆(LSTM)网络优化 VAR 参数,我们保留了用于预测的马尔可夫依赖性,并充分利用了 LSTM 的递归结构和强大的学习能力。为了确保模型的稳定性,我们使用安斯利-科恩变换对自回归系数强制执行因果关系条件。我们利用从数据中生成的现实曲线对估计能力进行了模拟研究。我们将该模型扩展到具有时变参数的综合 VAR,并将其应用于能源价格数据时的预测性能与现有方法进行了比较。
{"title":"DeepTVAR: Deep learning for a time-varying VAR model with extension to integrated VAR","authors":"Xixi Li, Jingsong Yuan","doi":"10.1016/j.ijforecast.2023.10.001","DOIUrl":"10.1016/j.ijforecast.2023.10.001","url":null,"abstract":"<div><p>This paper proposes a new approach called DeepTVAR that employs a deep learning methodology for vector autoregressive (VAR) modeling and prediction with time-varying parameters. By optimizing the VAR parameters with a long short-term memory (LSTM) network, we retain the Markovian dependence for prediction purposes and make full use of the recurrent structure and powerful learning ability of the LSTM. To ensure the stability of the model, we enforce the causality condition on the autoregressive coefficients using the Ansley–Kohn transform. We provide a simulation study of the estimation ability using realistic curves generated from data. The model is extended to integrated VAR with time-varying parameters, and we compare its forecasting performance with existing methods when applied to energy price data.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023001000/pdfft?md5=bc81dadfc6183648fd77733111eafc20&pid=1-s2.0-S0169207023001000-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136153899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-28DOI: 10.1016/j.ijforecast.2023.10.005
Jesús Gonzalo , Jean-Yves Pitarakis
This paper is concerned with detecting the presence of out-of-sample predictability in linear predictive regressions with a potentially large set of candidate predictors. We propose a procedure based on out-of-sample MSE comparisons that is implemented in a pairwise manner using one predictor at a time. This results in an aggregate test statistic that is standard normally distributed under the global null hypothesis of no linear predictability. Predictors can be highly persistent, purely stationary, or a combination of both. Upon rejecting the null hypothesis, we introduce a predictor screening procedure designed to identify the most active predictors. An empirical application to key predictors of US economic activity illustrates the usefulness of our methods. It highlights the important forward-looking role played by the series of manufacturing new orders.
{"title":"Out-of-sample predictability in predictive regressions with many predictor candidates","authors":"Jesús Gonzalo , Jean-Yves Pitarakis","doi":"10.1016/j.ijforecast.2023.10.005","DOIUrl":"10.1016/j.ijforecast.2023.10.005","url":null,"abstract":"<div><p>This paper is concerned with detecting the presence of out-of-sample predictability in linear predictive regressions with a potentially large set of candidate predictors. We propose a procedure based on out-of-sample MSE comparisons that is implemented in a pairwise manner using one predictor at a time. This results in an aggregate test statistic that is standard normally distributed under the global null hypothesis of no linear predictability. Predictors can be highly persistent, purely stationary, or a combination of both. Upon rejecting the null hypothesis, we introduce a predictor screening procedure designed to identify the most active predictors. An empirical application to key predictors of US economic activity illustrates the usefulness of our methods. It highlights the important forward-looking role played by the series of manufacturing new orders.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023001048/pdfft?md5=80ff4bc94530f3c1aff904ea06341ce6&pid=1-s2.0-S0169207023001048-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136119733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}