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学者姓名：卓杏轩
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Abstract ：
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 等. "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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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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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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本研究对福建省智能制造发展的研究背景、发展现状及存在问题进行分析,确定福建省未来五年甚至十五年的发展目标以及战略定位。研究认为,新时期新阶段福建省仍然要以"产业转型升级"为主线,以智能制造为主攻方向,加快推动智能制造高质量跨越式发展,增强制造业"韧性",推动制造业高质量发展和转型升级,力争实现整体性突破。
Keyword ：
制造强国 制造强国 发展战略 发展战略 智能制造 智能制造 福建省 福建省
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| GB/T 7714 | 周济 , 付贤智 , 李培根 et al. 福建省智能制造发展战略与实施路径研究 [J]. | 学会 , 2021 , (08) : 4-10 . |
| MLA | 周济 et al. "福建省智能制造发展战略与实施路径研究" . | 学会 08 (2021) : 4-10 . |
| APA | 周济 , 付贤智 , 李培根 , 俞建勇 , 李德群 , 黄志刚 et al. 福建省智能制造发展战略与实施路径研究 . | 学会 , 2021 , (08) , 4-10 . |
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在总结福建省新兴产业发展成效和存在问题等的基础上,提出福建省新兴产业发展思路与布局,针对重点产业梳理了其重点发展领域,并指出了"十四五"时期和"面向2035年"的重点方向,最后提出了保障目标实现的重点措施。
Keyword ：
产业集群 产业集群 发展战略 发展战略 新兴产业 新兴产业 福建省 福建省
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| GB/T 7714 | 尤政 , 付贤智 , 冯记春 et al. 福建新兴产业发展战略研究 [J]. | 学会 , 2021 , (08) : 11-18 . |
| MLA | 尤政 et al. "福建新兴产业发展战略研究" . | 学会 08 (2021) : 11-18 . |
| APA | 尤政 , 付贤智 , 冯记春 , 黄志刚 , 郭太良 , 周源 et al. 福建新兴产业发展战略研究 . | 学会 , 2021 , (08) , 11-18 . |
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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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Text of abstract In the big data era, it is common to encounter data observed at different frequencies. This raises the problem of how to explore the heterogeneous nonlinear relationship between variables on mixed sampling frequency data. To this end, we develop a novel quantile regression neural network for mixed sampling frequency data called QRNN-MIDAS by introducing the Mixed Data Sampling (MIDAS) technique into the framework of quantile regression neural network (QRNN). The proposed QRNN-MIDAS model enables QRNN to handle raw mixed sampling frequency data directly. Specifically, we conduct frequency alignment on each high frequency input variable according to the given maximum lag order. Then, a convenient parametric weight function is imposed on the frequency alignment vector and a low frequency variable is obtained. This strategy allows the QRNN-MIDAS model to extract valuable information from raw mixed sampling frequency data, which is helpful to explore the heterogeneous nonlinear relationship between variables in real time. To illustrate the efficacy of QRNNMIDAS, both Monte Carlo simulation studies and real-world applications are considered. The numerical results show that the QRNN-MIDAS model outperforms several competing models in terms of goodness of-fit and predictive ability. In addition, US quarterly GDP growth and China's monthly inflation forecast results also illustrate the superiority of the QRNN-MIDAS model, and provide more timely, accurate and comprehensive forecasts for decision-making. (c) 2021 Elsevier B.V. All rights reserved.
Keyword ：
GDP forecasting GDP forecasting Inflation forecasting Inflation forecasting Mixed sampling frequency data Mixed sampling frequency data Neural network Neural network Nonlinearity Nonlinearity Quantile regression Quantile regression
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| GB/T 7714 | Xu, Qifa , Liu, Shuting , Jiang, Cuixia et al. QRNN-MIDAS: A novel quantile regression neural network for mixed sampling frequency data [J]. | NEUROCOMPUTING , 2021 , 457 : 84-105 . |
| MLA | Xu, Qifa et al. "QRNN-MIDAS: A novel quantile regression neural network for mixed sampling frequency data" . | NEUROCOMPUTING 457 (2021) : 84-105 . |
| APA | Xu, Qifa , Liu, Shuting , Jiang, Cuixia , Zhuo, Xingxuan . QRNN-MIDAS: A novel quantile regression neural network for mixed sampling frequency data . | NEUROCOMPUTING , 2021 , 457 , 84-105 . |
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Practically, supply disruption may lead production process to entirely halt (completely disrupted) or the output to differ in the order size (partially disrupted), which makes it more difficult for the retailer to satisfy stochastic market demand. Under the circumstance, the retailer is likely to procure products from two suppliers to effectively alleviate the demand-supply mismatches. Thus, under supply disruption and stochastic demand, this paper develops both backup sourcing and simultaneous sourcing (SS) strategies to analyze the retailer's performance, where backup sourcing includes wholesale price priority (WPP) and supply reliability priority (SRP). Specifically, (1) under WPP, when the selling price is relatively lower (higher), the retailer is suggested to activate the reliable backup supplier after the realization of supply disruption (demand uncertainty). (2) Under SRP, two scenarios including minor disruption and major disruption can be identified, where the retailer's order quantity from the reliable (unreliable) supplier under minor disruption scenario is more (less) than that under major. (3) Finally, this paper systematically compares the retailer's preferences among WPP, SRP, and SS via theoretical results and numerical examples. That is, when the unreliable supplier is more likely to work normally or shortage cost (selling price) is relatively lower, the retailer prefers SPR regarding the unreliable supplier as backup sourcing due to its lower wholesale price and acceptable supply disruption. Otherwise, the retailer is inclined to WPP regarding the reliable supplier as backup sourcing for ensuring all market demand to be satisfied. In addition, unless the emergency prices of two suppliers are extremely higher, backup sourcing strategies could perform better than simultaneous sourcing strategy.
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| GB/T 7714 | Lin, Feng , Shi, Jinzhao , Wu, Peng et al. Retailer's Optimal Procurement Strategy under Supply Disruption and Stochastic Demand: Backup Sourcing or Simultaneous Sourcing [J]. | COMPLEXITY , 2020 , 2020 . |
| MLA | Lin, Feng et al. "Retailer's Optimal Procurement Strategy under Supply Disruption and Stochastic Demand: Backup Sourcing or Simultaneous Sourcing" . | COMPLEXITY 2020 (2020) . |
| APA | Lin, Feng , Shi, Jinzhao , Wu, Peng , Zhuo, Xingxuan . Retailer's Optimal Procurement Strategy under Supply Disruption and Stochastic Demand: Backup Sourcing or Simultaneous Sourcing . | COMPLEXITY , 2020 , 2020 . |
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