居民杠杆率的发展风险、国际比较与治理路径

当前,我国居民杠杆率快速上涨,并呈现出与国际趋势背离、风险不对称、居民储蓄率下降、债务过度扩张等风险特征.从居民杠杆率的发展肇因看,可归于中长期消费贷款领域的货币政策宽松促使房地产贷款过热,以及短期消费贷款领域的金融科技驱动消费金融机构的野蛮生长.通过采用法律金融学理论,重点分析不同法系下居民杠杆率的发展演进,充分借鉴海洋法系(以美国为代表)与大陆法系(以日本为代表)的居民杠杆率高企的风险治理经验,结合我国居民部门的风险境况,提出我国防范居民杠杆率高企的风险治理路径.     

Understanding sustainable development flexibility: An information perspective

Although flexibility has been considered critical in responding to uncertainty in a business environment, few studies have explored firms' flexibility in sustainable development. To understand the nature of firms that can respond better to uncertainty in their sustainable development practices, this study defines sustainable development flexibility and investigates the mechanism underlying its formation. The study proposes a conceptual framework on the interactions of managers' environmental attitude and cognitive style, as well as firms' information integration on sustainable development flexibility. A questionnaire survey was developed to test the corresponding hypotheses, and 241 valid responses were received from middle- and top-level managers in Chinese firms. The results show that (a) the higher the manager's environmental attitude, the higher the firm's information integration and greater sustainable supply chain flexibility, and (b) the more intuitive the manager's cognitive style, the greater the impact of environmental attitude on sustainable development flexibility.     

深度学习方法在作物遥感分类中的应用和挑战

[目的]准确估算作物的面积和分布对粮食安全至关重要。与传统的机器学习方法相比,深度学习具有多种优势,如端到端训练、可迁移性。为有效利用高时空数据进行作物识别提供了新的机遇。已有多种模型被应用于作物分类任务中,针对不同的分类任务,如何有效地选择模型,并对其进行训练和使用已成为关键问题。[方法]文章回顾了利用深度学习模型对作物分类的主要研究。N维卷积神经网络(N-D CNN)(N=1、2、3)和递归神经网络(RNN)已被有效用于作物分类任务。长短期记忆RNN(LSTM RNN)和门控循环单元RNN(GRU RNN)是RNN的变体,解决了随着时间序列增加RNN出现的梯度消失或爆炸问题。此外,还有研究使用CNN和RNN(我们称为RCNN)的混合模型对作物进行分类。该文首先阐述了使用深度学习方法进行作物制图的背景和意义,并介绍了CNN和RNN模型结构。然后回顾了一些典型的研究,包括模型的结构、遥感数据源、数据处理方法和分类精度。最后,总结了使用深度学习方法进行作物分类的挑战以及现有解决方案的局限性。[结果](1)1-D CNN可用于提取时间特征,或时间+光谱特征,分类效果良好;2-D CNN已被广泛应用于单时相数据的空间特征提取,分类精度依赖于数据源;3-D CNN应用较少,但具有很大的潜力,尤其是时间+空间维度的特征提取;(2)相同条件下(架构、数据源、研究区域、类别),LSTM RNN和GRU RNN分类效果通常高于普通RNN,而前两者的效果差距不大,但GRU RNN训练时间较短;(3)CNN+RNN混合模型(RCNN)用RNN比3-D CNN更适合提取时间特征。这主要是由于RNN建立了对序列数据的长期依赖,而3-D CNN卷积核是局部计算的。[结论]通过分析,认为深度学习技术是作物遥感分类的有效工具。此外,与其他模型相比,RCNN,3-D CNN和GUR RNN具有更大的潜力。     

产学研合作创新绩效影响因素元分析研究

为探寻中国产学研合作创新绩效影响因素，挖掘促进中国产学研深度融合的发力点,运用元分析方法，对中国国内与产学研合作创新绩效影响因素相关文献进行再分析，结果发现：①企业层面的企业规模、企业知识吸收能力、企业研发人员投入与企业研发经费投入，组织间层面的知识协同性、知识转移能力、信息沟通渠道及合作紧密度，政府层面的政策支持与经费支持，区域层面的法律法规完善性与经济发展水平均能够显著提升产学研合作创新绩效；②区位因素、数据类型分别对产学研合作创新绩效与企业规模、政府经费资助关系具有显著调节作用。     

Childhood adversity and the propensity for entrepreneurship: A quasi-experimental study of the Great Chinese Famine

Studies on the determinants of entrepreneurship emphasize that challenged adults tend to become entrepreneurs. However, research has not addressed the childhood origins surrounding the propensity for entrepreneurship. This article links childhood adversity to the propensity of individuals to become migrant entrepreneurs later in life. We test hypotheses derived from this theory in the context of whether, and when, children who survived the Great Chinese Famine of 1959–1961 became migrant entrepreneurs. Results strongly indicate that those who survived greater hardship during the Famine are more likely to become entrepreneurs, especially when they were younger during the famine years. We also find that being younger at the time of migration increased the likelihood of becoming entrepreneurs in their new locale. Overall, this study casts light on why, how and when childhood adversity shapes the propensity for entrepreneurship.     

财政政策与货币政策的动态调控效应*——基于时变参数向量自回归模型的检验

本文廓清了财政政策与货币政策搭配动态调控宏观经济的机理，揭示了两者协调影响经济的“黑箱”机制及其在经济不同阶段的搭配方式；运用中国2004—2019年的经济季度数据，构建TVP SV VAR模型探究两类政策对宏观经济的调控效应。研究发现：财政政策与货币政策共同把控流动性“闸门”实现互动协调；财政政策搭配货币政策具有时变性，在整体上“同向发力”推动经济发展；两类政策对产出的影响并非始终如理论一致，在结构层面仍有优化靶向性操作的余地。     

关系质量对企业知识创造绩效的作用机理：一个被调节的中介模型

基于嵌入性视角，分别引入知识转移、合作模式作为中介变量和调节变量，深入探究关系质量影响企业知识创造绩效的内在机理。利用277份来自全国多地的制造业及高新技术企业调查问卷，采用多元回归方法进行实证研究。结果表明：经济型和社会型关系质量均正向影响企业知识创造绩效；社会型关系质量通过元素知识和架构知识转移正向影响企业知识创造绩效。在契约治理模式下，经济型关系质量更倾向于通过元素知识转移正向影响企业知识创造绩效；在股权治理模式下，社会型关系质量更倾向于通过架构知识转移正向影响企业知识创造绩效。     

When Targets Strike Back: How Negative Workplace Gossip Triggers Political Acts by Employees

Journal of Business Ethics - This study examines why and when negative workplace gossip promotes self-serving behaviors by the employees being targeted. Using conservation of resources (COR)...     

An Empirical Analysis of Green Experiential Loyalty: A Case Study

The purpose of this study is to elaborate the structural relationships among the experiential quality dimensions, experiential trust, emotional attachment, experiential image, switching experience, experiential satisfaction, and experiential loyalty from the green perspective. The study findings are based on structural equation modeling (SEM) analysis of a convenience sample of 560 customers at one green restaurant in Taipei City, Taiwan. The findings show that investing resources in an increase in the experiential quality dimensions and a decrease in green switching experience is useful to enhance green experiential trust, green emotional attachment, green experiential image, green experiential satisfaction, and green experiential loyalty.     

Option pricing based on hybrid GARCH-type models with improved ensemble empirical mode decomposition

The exploration of option pricing is of great significance to risk management and investments. One important challenge to existing research is how to describe the underlying asset price process and fluctuation features accurately. Considering the benefits of ensemble empirical mode decomposition (EEMD) in depicting the fluctuation features of financial time series, we construct an option pricing model based on the new hybrid generalized autoregressive conditional heteroskedastic (hybrid GARCH)-type functions with improved EEMD by decomposing the original return series into the high frequency, low frequency and trend terms. Using the locally risk-neutral valuation relationship (LRNVR), we obtain an equivalent martingale measure and option prices with different maturities based on Monte Carlo simulations. The empirical results indicate that this novel model can substantially capture volatility features and it performs much better than the M-GARCH and Black–Scholes models. In particular, the decomposition is consistently helpful in reducing option pricing errors, thereby proving the innovativeness and effectiveness of the hybrid GARCH option pricing model.