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学者姓名：卓杏轩
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Abstract ：
As the Belt and Road Initiative (BRI) continues to advance, trade networks among BRI countries have evolved significantly. Understanding development patterns within these trade networks is crucial for promoting further growth. This study adopts a spatiotemporal perspective to analyze the dynamic evolution and driving factors of trade networks among BRI countries, utilizing the Separable Temporal Exponential Random Graph Model (STERGM) and a change point detection model. These methods assess the impact of endogenous structural variables, exogenous edge-level covariates, and exogenous nodal variables on the formation and dissolution of trade networks, as well as on stage-specific changes within these networks. The findings reveal that: (1) around 2017, the trade networks underwent a significant shift, with high-trade-value relationships growing faster than low-trade-value ones, and the networks have a small-world character. (2) China, Turkey, India, and Russia hold central positions in the trade networks, functioning as "bridges" and "hubs"; the prominence of Poland, the Czech Republic, and Ukraine has increased, while Thailand and United Arab Emirates have seen a relative decline; (3) geographical proximity, bilateral investment treaties, and shared legal origins foster trade network development, whereas exchange rate volatility and political distance have a negative impact. Countries with high urbanization, large populations, and strong economies are more likely to form trade relations. And these effects on the formation and maintenance of trade relations changed significantly before and after 2017. Therefore, while enhancing their own economic and social development, BRI countries should work to strengthen trade relations by bridging political differences and establishing trade agreements.
Keyword ：
Change-point detection Change-point detection Graph Model Graph Model Separable Temporal Exponential Random Separable Temporal Exponential Random The Belt and Road initiative The Belt and Road initiative Trade networks Trade networks
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| GB/T 7714 | Zhuo, Xingxuan , Lin, Liuqing , Lian, Jiefan . Spatiotemporal analysis of the dynamic evolution and driving factors of trade networks in the Belt and Road countries [J]. | SOCIAL NETWORKS , 2025 , 82 : 80-98 . |
| MLA | Zhuo, Xingxuan 等. "Spatiotemporal analysis of the dynamic evolution and driving factors of trade networks in the Belt and Road countries" . | SOCIAL NETWORKS 82 (2025) : 80-98 . |
| APA | Zhuo, Xingxuan , Lin, Liuqing , Lian, Jiefan . Spatiotemporal analysis of the dynamic evolution and driving factors of trade networks in the Belt and Road countries . | SOCIAL NETWORKS , 2025 , 82 , 80-98 . |
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High-frequency macro-financial environment variables provide more useful information and are efficient in predicting the low-frequency GDP growth rate. To this end, we extend the traditional Growth-at-Risk (GaR) into a high-frequency GaR (HF-GaR). In this extension, we construct three high-frequency macro-financial environment indices using a mixed frequency dynamic factor model and then use a mixed data sampling-quantile regression method to measure China's daily GaR from Jan 1, 2000, to Sep 30, 2024. The evidence shows that our HF-GaR has favorable prediction performance, with quantile mean absolute error and quantile root square error values less than 0.1 and is significantly superior to the traditional GaR at the 1% level for most quantiles. Additionally, HF-GaR can offer early warning of economic downturns, especially predicting China's GDP growth rate at the 5% quantile less than 0 in 2020Q1. Moreover, we conduct a counterfactual scenario analysis and find that the conditional quantile of GDP growth rate changes as the macro-financial environment tightens or loosens. Finally, we also validated that the HF-GaR model is equally applicable in other economies.
Keyword ：
Counterfactual scenario analysis Counterfactual scenario analysis Growth-at-Risk Growth-at-Risk MF-DFM MF-DFM MIDAS-QR MIDAS-QR Skewed t-distribution Skewed t-distribution
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| GB/T 7714 | Xu, Mengnan , Xu, Qifa , Jiang, Cuixia et al. High-frequency Growth-at-Risk of China: the Role of Macro-financial Environment [J]. | COMPUTATIONAL ECONOMICS , 2025 . |
| MLA | Xu, Mengnan et al. "High-frequency Growth-at-Risk of China: the Role of Macro-financial Environment" . | COMPUTATIONAL ECONOMICS (2025) . |
| APA | Xu, Mengnan , Xu, Qifa , Jiang, Cuixia , Zhuo, Xingxuan . High-frequency Growth-at-Risk of China: the Role of Macro-financial Environment . | COMPUTATIONAL ECONOMICS , 2025 . |
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This paper introduces a novel forecasting approach that addresses a significant challenge in applied research: effectively utilizing high-dimensional and mixed-frequency data from multiple sources to explain and predict variables that respond at high frequency. This approach combines a mixed data sampling model and group variable selection methods, resulting in the development of the Group Penalized Reverse Unrestricted Mixed Data Sampling Model (GP-RU-MIDAS). The GP-RU-MIDAS model is designed to achieve various research objectives, including analyzing mixed-frequency data in reverse, estimating high-dimensional parameters, identifying key variables, and analyzing their relative importance and sensitivity. By applying this model to uncover uncertainties in stock market returns, the following notable results emerge: (1) GP-RU-MIDAS improves the selection of relevant variables and enhances forecasting accuracy; (2) various risks impact stock market returns in diverse ways, with effects varying over time and exhibiting continuous trends, phase shifts, or extreme levels; and (3) stock market volatility and the Euro to RMB exchange rate significantly influence stock market returns over different forecasting periods, with a generally positive and dynamic impact. In conclusion, the GP-RU-MIDAS model demonstrates robustness and utility in complex data analysis scenarios, providing insights into the nuanced realm of stock market risk assessment.
Keyword ：
group penalties group penalties high-dimensional data high-dimensional data mixed data sampling model mixed data sampling model mixed-frequency data mixed-frequency data stock market returns stock market returns stock market risks stock market risks
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| GB/T 7714 | Zhuo, Xingxuan , Luo, Shunfei , Cao, Yan . Exploring Multisource High-Dimensional Mixed-Frequency Risks in the Stock Market: A Group Penalized Reverse Unrestricted Mixed Data Sampling Approach [J]. | JOURNAL OF FORECASTING , 2024 , 44 (2) : 459-473 . |
| MLA | Zhuo, Xingxuan et al. "Exploring Multisource High-Dimensional Mixed-Frequency Risks in the Stock Market: A Group Penalized Reverse Unrestricted Mixed Data Sampling Approach" . | JOURNAL OF FORECASTING 44 . 2 (2024) : 459-473 . |
| APA | Zhuo, Xingxuan , Luo, Shunfei , Cao, Yan . Exploring Multisource High-Dimensional Mixed-Frequency Risks in the Stock Market: A Group Penalized Reverse Unrestricted Mixed Data Sampling Approach . | JOURNAL OF FORECASTING , 2024 , 44 (2) , 459-473 . |
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The impact of financial conditions on Chinese macroeconomic activities has recently received considerable attention. This paper utilizes a constructed Chinese financial conditions index (FCI) to appraise the role of financial conditions in Chinese growth at risk, and further traces the influencing factors of tail risks of macroeconomic activities. The findings reveal that financial conditions may lead to an increase in future tail risks for macroeconomic activities, and financial conditions are associated more with downside risks than with upside potential. Moreover, the extension degree of financial conditions in relation to the tail risks of macroeconomic activities displays time-varying and heterogeneous characteristics. In particular, financial conditions have a more pronounced effect on the tail risks of investment growth. Additionally, this paper provides direct evidence from a financial perspective, suggesting that M2 is a common factor of the tail risks of macroeconomic activities, and treasury yields play a crucial role in tail risks related to consumption growth. Simultaneously, the real effective exchange rate of the Renminbi Yuan emerges as a vital factor in tail risks regarding import and export growth. Our results provide valuable insights for the government in addressing macroeconomic risks and formulating relevant policies.
Keyword ：
Deep GaR Deep GaR Financial conditions Financial conditions Generalized variance decompositions Generalized variance decompositions Macroeconomic activities Macroeconomic activities Tail risk Tail risk
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| GB/T 7714 | Liu, Han , Wang, Lijun , Zhuo, Xingxuan . Unveiling the shadows: The effects of financial conditions on the tail risks of China's macroeconomic activities [J]. | ECONOMIC ANALYSIS AND POLICY , 2024 , 85 : 1-14 . |
| MLA | Liu, Han et al. "Unveiling the shadows: The effects of financial conditions on the tail risks of China's macroeconomic activities" . | ECONOMIC ANALYSIS AND POLICY 85 (2024) : 1-14 . |
| APA | Liu, Han , Wang, Lijun , Zhuo, Xingxuan . Unveiling the shadows: The effects of financial conditions on the tail risks of China's macroeconomic activities . | ECONOMIC ANALYSIS AND POLICY , 2024 , 85 , 1-14 . |
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本文采用阈值组惩罚无约束混频数据抽样（T-GP-U-MIDAS）模型对中国季度实际GDP增长率进行实时预报和短期预测，并研究其精度提升机理。实证结果显示：与AR等传统模型相比，T-GP-U-MIDAS模型在预测精度方面具有显著的比较优势；T-GP-U-MIDAS模型通过引入具有先行特征的阈值变量识别经济运行中可能存在的转变点，并依据不同的经济状况筛选出对预测实际GDP增长率存在重要影响的核心变量，在一定程度上提高了模型的预测和解释能力，是现有混频数据模型的有效改进和补充。
Keyword ：
T-GP-U-MIDAS模型 T-GP-U-MIDAS模型 实际GDP增长率 实际GDP增长率 高维混频数据 高维混频数据
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| GB/T 7714 | 刘汉 , 刘营 , 卓杏轩 . 基于高维混频信息甄选的宏观经济总量实时预报与短期预测 [J]. | 经济学(季刊) , 2023 , 23 (03) : 1112-1130 . |
| MLA | 刘汉 et al. "基于高维混频信息甄选的宏观经济总量实时预报与短期预测" . | 经济学(季刊) 23 . 03 (2023) : 1112-1130 . |
| APA | 刘汉 , 刘营 , 卓杏轩 . 基于高维混频信息甄选的宏观经济总量实时预报与短期预测 . | 经济学(季刊) , 2023 , 23 (03) , 1112-1130 . |
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Under the combined effects of inventory-level-dependent demand (ILDD) and trade credit, the retailer is able to order more quantities to stimulate market demand. However, from the supplier's perspective, two important issues are lacking sufficient attention. First, during the credit period, the retailer's higher order quantities imply increases in both the retailer's account payable and the supplier's opportunity cost of capital. Second, given the supplier's fixed production rate, the increased market demand may drive the capacity utilization to be variable. Thus, by formulating a supplier-dominated system, this paper incorporates trade credit limit (TCL) to address its effects on optimal policies vis-a ⠁-vis the item with ILDD. Specifically, three indicators can be proposed to reveal which type of financing policy the retailer should choose. Moreover, based on TCL, the supplier can effectively manage the retailer's order quantity and the corresponding account payable. Additionally, the retailer's maximum allowable order quantity is developed to ensure that the supplier can supply the retailer's order quantity on time. Furthermore, when the ef-fects of ILDD become more significant, the manufacturer will reduce the maximum allowable order quantity to control the retailer's order incentive.& COPY; 2023 China Science Publishing & Media Ltd. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keyword ：
Delay in payments Delay in payments Inventory -level -dependent demand Inventory -level -dependent demand Order policy Order policy Supplier -dominated channel Supplier -dominated channel Trade credit limit Trade credit limit
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| GB/T 7714 | Lin, Feng , Shi, Yongyan , Zhuo, Xingxuan . Optimizing order policy and credit term for items with inventory-level-dependent demand under trade credit limit [J]. | JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING , 2023 , 8 (4) : 413-429 . |
| MLA | Lin, Feng et al. "Optimizing order policy and credit term for items with inventory-level-dependent demand under trade credit limit" . | JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING 8 . 4 (2023) : 413-429 . |
| APA | Lin, Feng , Shi, Yongyan , Zhuo, Xingxuan . Optimizing order policy and credit term for items with inventory-level-dependent demand under trade credit limit . | JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING , 2023 , 8 (4) , 413-429 . |
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Historical tourism volume, search engine data, and weather calendar data have close causal relationship with daily tourism volume. However, when used in the prediction of daily tourism volume, the feature variables of the huge and complex search engine data do not have strong independence. These repetitive and highly relevant data must be analyzed and selected; otherwise, they will increase the training burden of neural network and reduce the prediction effect. This study proposes a daily tourism volume prediction model, maximum correlation minimum redundancy feature selection and long short-term memory, on the basis of feature selection and deep learning. Firstly, the multivariate high-dimensional features, including search engine data and weather factors, are selected to identify the key influencing factors. Secondly, the deep neural network is used to make a multistep forward rolling prediction of daily tourism volume. Results show that keywords of famous scenic spots, weather, historical tourism volume, and tourism strategies in the search engine data significantly improve the prediction accuracy of daily tourism volume. The proposed maximum correlation minimum redundancy feature selection and long short-term memory model performs better than other models, such as autoregressive integrated moving average, multiple regression, support vector machine, and long short-term memory.
Keyword ：
daily tourism volume prediction daily tourism volume prediction deep learning deep learning feature selection feature selection search engine data search engine data
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| GB/T 7714 | Yin, Ming , Lu, Feiya , Zhuo, Xingxuan et al. Prediction of daily tourism volume based on maximum correlation minimum redundancy feature selection and long short-term memory network [J]. | JOURNAL OF FORECASTING , 2023 , 43 (2) : 344-365 . |
| MLA | Yin, Ming et al. "Prediction of daily tourism volume based on maximum correlation minimum redundancy feature selection and long short-term memory network" . | JOURNAL OF FORECASTING 43 . 2 (2023) : 344-365 . |
| APA | Yin, Ming , Lu, Feiya , Zhuo, Xingxuan , Yao, Wangzi , Liu, Jialong , Jiang, Jijiao . Prediction of daily tourism volume based on maximum correlation minimum redundancy feature selection and long short-term memory network . | JOURNAL OF FORECASTING , 2023 , 43 (2) , 344-365 . |
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A one-step forward forecasting test for carbon market pricing is done in this work, in which data from the first seven trading days is used to anticipate the price on the eighth trading day. The study compares the MAE, MSE, and RMSE values of several forecasting models and discovers that combining empirical mode decomposition (EMD) and forecasting models yields the best results. It was shown that the hybrid model can improve both the durability and accuracy of carbon price estimates. In terms of forecasting, the combined EMD-BiLSTM-ATTENTION model beats other comparator models, and carbon price forecasting errors in Hubei and Fujian are smaller than those in Shenzhen due to their more stable frequency amplitudes. Nevertheless, it has been discovered that estimating the carbon price in Shenzhen is more difficult due to higher amplitude variations, resulting in higher prediction errors. Overall, the findings indicate that the proposed EMD-BiLSTM-ATTENTION model is appropriate for carbon price prediction, and the study includes carbon market price prediction maps for Shenzhen, Hubei, and Fujian. © 2023 IEEE.
Keyword ：
Carbon Carbon Commerce Commerce Empirical mode decomposition Empirical mode decomposition Forecasting Forecasting Long short-term memory Long short-term memory
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| GB/T 7714 | Li, Tong , Li, Shilun , Lin, Feng et al. Carbon Price Prediction based on EMD-BiLSTM-ATTENTION model [C] . 2023 : 90-94 . |
| MLA | Li, Tong et al. "Carbon Price Prediction based on EMD-BiLSTM-ATTENTION model" . (2023) : 90-94 . |
| APA | Li, Tong , Li, Shilun , Lin, Feng , Zhuo, Xingxuan . Carbon Price Prediction based on EMD-BiLSTM-ATTENTION model . (2023) : 90-94 . |
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In a co-opetitive supply chain, an upstream contract manufacturer (CM) is more likely to not only supply specified products to an original equipment manufacturer (OEM) but also to directly compete with the OEM by offering customers substitutable products. Further, when supply uncertainty is involved, the OEM will face not only more fierce competition in the downstream market but also more unreliable delivery of components. Thus, in a co-opetitive supply chain, this paper assumes that the OEM adopts in-house production when outsourcing products from the CM with supply uncertainty. Specifically, in this paper, the CM's effective production cost is firstly defined to quantify impacts of supply uncertainty on the equilibrium of channel structures. Based on this, the insourcing effect is proposed firstly to measure the incentive for the OEM to adopt in-house production, and the marketing gap effect is firstly proposed to indicate when the CM adopts direct selling. Further, under joint influences of these effects, equilibriums of channel structures of a co-opetitive supply chain can be correctly proven. Additionally, several exten-sions are involved to indicate that the OEM is still able to adopt in-house production even the CM can assume termination or the second-mover advantage. (c) 2021 Elsevier Ltd. All rights reserved.
Keyword ：
Channel structure Channel structure Co-opetitive supply chain Co-opetitive supply chain Direct selling Direct selling In-house production In-house production Operations management Operations management Supply uncertainty Supply uncertainty
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| GB/T 7714 | Lin, Feng , Qin, Xibei , Pu, Xujin et al. Effects of in-house production on channel structures in a co-opetitive supply chain under supply uncertainty [J]. | OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE , 2021 , 103 . |
| MLA | Lin, Feng et al. "Effects of in-house production on channel structures in a co-opetitive supply chain under supply uncertainty" . | OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 103 (2021) . |
| APA | Lin, Feng , Qin, Xibei , Pu, Xujin , Zhu, Weiwei , Zhuo, Xingxuan . Effects of in-house production on channel structures in a co-opetitive supply chain under supply uncertainty . | OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE , 2021 , 103 . |
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Practically, two-level trade credit is usually applied to stimulate the retailer's order incentive for deteriorating items with expiration date. Nevertheless, previous studies ignore to identify both quantity loss and quality loss derived from expiration date, and lack of examining whether these joint losses impact effectiveness of two-level trade credit. Thus, this paper firstly builds an inventory model for deteriorating items with expiration date, which accurately determines the retailer's optimal response when involving both quantity and quality losses. (1) For given expiration date, this paper identifies the phenomenon of limit effect and its occurred conditions, referring that the retailer's optimal order cycle for deteriorating items equals its expiration date; (2) this paper validates the scope of limit effect, below which deteriorating items should be optimally replenished with its expiration date. In addition, two-level trade credit is further incorporated into deteriorating items, resulting in two types of expiration date scenarios including lower and higher. Specifically, for the given expiration date, upstream credit period can affect the retailer's selection of operational policies, and the occurrence of limit effect. And along with the increase of expiration date, more operational policies can be assumed by the retailer.
Keyword ：
deterioration deterioration expiration date expiration date finance finance Inventory Inventory logistics logistics trade credit trade credit
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| GB/T 7714 | Lin, Feng , Wu, Peng , Shi, Jinzhao et al. Impacts of expiration date on optimal ordering policy for deteriorating items under two-level trade credit: Quantity loss and quality loss [J]. | JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY , 2021 , 73 (6) : 1393-1410 . |
| MLA | Lin, Feng et al. "Impacts of expiration date on optimal ordering policy for deteriorating items under two-level trade credit: Quantity loss and quality loss" . | JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 73 . 6 (2021) : 1393-1410 . |
| APA | Lin, Feng , Wu, Peng , Shi, Jinzhao , Tao, Jia , Zhuo, Xingxuan . Impacts of expiration date on optimal ordering policy for deteriorating items under two-level trade credit: Quantity loss and quality loss . | JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY , 2021 , 73 (6) , 1393-1410 . |
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