资本外逃的经济效应与应对政策选择研究

资本外逃是一个复杂的经济问题,是由多种原因造成的,并会给一国经济发展带来不可估量的风险和损失。基于此,论文梳理了资本外逃的研究现状,提出了资本外逃所产生的经济效应,在此基础上,提出了资本外逃的政策选择,为一国在治理资本外逃时提供了政策选择和借鉴。     

新能源材料物理化学课程教学改革探究

新能源材料物理化学课程内容抽象、概念多、涉及面广、公式应用严格,与生活和科学前沿联系紧密。为提高课程教学质量,教师引入案例教学抓住课程核心;联系材料发展历史,增加课堂教学的趣味性;结合科学发展前沿,使教学内容能够与时俱进;注重概念和公式的融会贯通,增加材料调研大作业;线上线下教学模式相结合,采用多样化的教学方法及教学手段;通过形成性评价考核,加强教学过程监管。     

政府购买服务中高校参与及提升服务能力路径研究

高校是政府购买服务的承接方,但其对政府购买服务的响应还处在起步探索阶段。文章首先厘清政府购买服务中高校的角色定位,接着从外在驱动和内在诉求两个角度分析了高校参与政府购买服务的可行性,并在此基础上阐述高校参与政府采购服务的实施路径以及提升服务能力的路径。文章认为,高校应该把握好参与政府服务项目这个良好契机,以积极的态度参与政府购买服务,拓宽自身的发展路径,促成政府和高校实现双赢。     

科技服务业产出测算：指标确立、数据收集与未来预测

科技服务业产出测算是对科技服务业产出数据的统计与分析，加强科技服务业产出数据的规范统计和应用分析可为我国政府调整产业政策提供重要数据支撑。以科技服务业产出测算为研究对象，结合学者观点、国家标准及各级政府相关产业政策，提出将分支产业作为初始指标的横向统计思路，构建科技服务业产出测算指标体系。在数据收集方面，强调要做好“收”与“填”两个基本工作；在指标确立和数据收集基础上，进一步提出未来预测的两种方法，以此作为科技服务业产出数据预测的初步探索；提出应从统一数据统计标准、完善数据调查与公开制度、加强数据资源开发3个方面建立健全我国科技服务业产出数据测量与管理体制机制。     

新能源发电企业资产管理措施研究

新能源行业发展速度加快,其中新能源发电企业是现阶段该行业发展的重要组成部门,企业的管理模式和体制是保证行业稳定发展的关键因素.在新能源发电企业中拥有大量多种不同类型的资产,资产管理是影响企业经济效益提高的主要因素.基于此,本文分析了新能源发电企业的资产特点,探讨影响新能源发电企业资产管理的因素以及具体策略,以期可以为企业发展提供有效参考.     

How is Benevolent Leadership Linked to Employee Creativity? The Mediating Role of Leader–Member Exchange and the Moderating Role of Power Distance Orientation

Previous research has shown that virtuous leader behavior in the form of benevolent leadership has considerable impact on employee creativity. However, little is known as to how and under what conditions these constructs are linked. In the current research, we proposed and tested a moderated mediation model positing leader–member exchange (LMX) as a mediator, and employee power-distance orientation as a moderator of this relationship. Two studies were conducted to test our hypothesized model. In Study 1, repeated measured data collected from 284 Chinese employees in an information technology company demonstrated that benevolent leadership had a lagged effect on LMX. In Study 2, analyses of multisource and lagged data from 391 Chinese employees in 42 research and development teams, and their direct supervisors indicated that benevolent leadership was positively related to supervisor-rated employee creativity via LMX. In addition, the relationship between benevolent leadership and LMX was stronger for employees high in power-distance orientation. Theoretical implications of benevolent leadership’s research and practical contributions concerning promoting creativity in organizations where benevolent leaders prevail are also discussed.     

跨越竞争丛林与范式重构：全球治理改革进程中的疫苗产业创新治理

疫苗作为新冠疫情防控的最关键手段之一，已在各国抗疫中发挥积极影响。疫苗研发、生产、注射意愿以及全球分配的公平性与可及性等一系列问题日益受到世界关注。分析全球治理改革中，疫苗作为一种全球公共产品的创新治理问题，讨论疫苗产业发展特征与创新规律演化，剖析西方国家疫苗产业创新治理困境以及“竞争丛林”逻辑，阐释中国行动方案及对全球治理改革的贡献。最终提出疫苗产业创新治理机制对推动全球治理改革的政策含义。     

智能制造有利于增加企业技术创新投入吗——基于智能制造试点的准自然实验

智能制造是我国制造业转型升级的主攻方向,而如何准确客观评价智能制造的实施效果成为亟待解决的问题.从技术创新投入视角,将智能制造试点政策实施视为一次准自然实验,采用2011-2019年制造业上市企业面板数据,通过PSM-DID方法研究智能制造对企业技术创新投入的影响.研究结果表明,智能制造显著影响企业技术创新投入,即通过提升客户集成度、供应商集成度进而提高企业技术创新投入,且促进作用仅发生在民营企业和非高新技术行业中.该发现扩展了现有研究外延,同时,为解决当前国际困局和促进智能制造的持续推广提供了重要实践价值.     

Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.