创新要素投入产出效率随机变动测度与结果分析

创新要素配置在投入产出两个环节中生成大量不完全技术信息，投入产出效率随机变动更加明显。从投入产出数据集合中提炼共同因子，与不可观测变量构成预测器，建立因子增广向量自回归模型(FAVAR)，分析随机变动方差构成，测度投入产出效率随机变动程度。随机变动效应包括水平效应、稳定性效应和规模效应。投入产出效率自回归扰动项表示随机变动程度，将其细分为共同因子，计算预测器方差及其与投入产出效率的协方差。基于669家上市公司月度、季度、半年度和年度技术研发数据，比较分析投入产出效率随机变动程度及形成原因，引入脉冲响应法分析变量方差构成与变动特征，提出相关政策建议。     

管理层权力制衡强度、债务约束与企业创新投资

企业创新是国家经济可持续增长的关键，受到管理层意愿的影响，因而需要对内部经营者的权力进行制衡。以2010-2018年我国A股上市公司为研究样本，实证检验管理层权力制衡强度对企业创新投资的影响，以及不同债务约束情境下高商业信用配置、高负债水平的调节效应，此外，还考察了产权性质的差异化影响。研究表明，管理层权力制衡强度越大，企业创新投资水平越高；高商业信用强化了该促进作用，而高负债水平弱化了该促进作用。进一步研究发现，管理层权力制衡强度与企业创新投资的关系在民企中更显著；国企能够更好地获得和运用商业信用，使其高商业信用对该关系的强化效应更显著；民企具有更强的债务约束，其高负债水平对该关系的弱化效应更明显。     

Inequality and entrepreneurial thresholds

We explore the relationship between inequality and entrepreneurial activity. Drawing on cross-sectional data from a largescale survey of the economic conditions of individuals across India, we develop a number of dimensions of inequality to explore empirically how inequality interacts with entrepreneurship, operationalized as self-employment or as employing other people. We find compelling evidence that there are thresholds to becoming self-employed, and even more so to assembling the combinations of resources and personal attributes required to become an employer. Greater inequality leaves more people unable to make the transition to self-employment, leaving casual laboring as the occupation of necessity. At the same time, inequality increases the number of employers in a society, by concentrating resources - particularly land and finance - enough for significant numbers of people to be able to cross this higher threshold. Lastly, greater differentiation into social or religious groups curtails the ability to cross either entrepreneurial threshold, presumably by limiting the extent and benefits of social networks of value for entrepreneurship.     

Risk aversion,prudence and temperance: An experiment in gain and loss

We characterize the individual's attitude towards risk, prudence and temperance in the gain and loss domains. We analyze the links between the three features of preferences for a given domain and between domains for each feature of preferences. Consequently, the reflection effect, the mixed risk aversion and the risk apportionment, are key concepts of our study. We also display some determinants for risk aversion, prudence and temperance in each domain. To do this, we conducted a lab experiment with students eliciting risk aversion, prudence and temperance in the two domains, and collected information about each subject's characteristics.     

Forecasting unknown-unknowns by boosting the risk radar within the risk intelligent organisation

This theoretical perspective paper interprets (un)known-(un)known risk quadrants as being formed from both abstract and concrete risk knowledge. It shows that these quadrants are useful for categorising risk forecasting challenges against the levels of abstract and concrete risk knowledge that are typically available, as well as for measuring perceived levels of abstract and concrete risk knowledge available for forecasting in psychometric research. Drawing on cybersecurity risk examples, a case is made for refocusing risk management forecasting efforts towards changing unknown-unknowns into known-knowns. We propose that this be achieved by developing the ‘boosted risk radar’ as organisational practice, where suitably ‘risk intelligent’ managers gather ‘risk intelligence information’, such that the ‘risk intelligent organisation’ can purposefully co-develop both abstract and concrete risk forecasting knowledge. We also illustrate what this can entail in simple practical terms within organisations.     

我国“一带一路”沿线地区风险投资时空分布及影响因素研究

深入分析我国“一带一路”沿线地区风险投资时空格局及影响因素，有助于促进创新创业与企业发展、提高区域科技创新水平。运用空间分析方法和空间计量模型，对2003-2018年我国“一带一路”沿线139个城市风险投资的时空分布特征及影响因素进行分析，结果表明：①沿线地区的风险投资发展具有不均衡性，呈现为沿海高、西部低的分异格局；②风险投资的全局自相关系数均为正，呈现出明显的空间集聚特征；③风险投资机构数、国家高新区数量、风险投资退出、政府引导基金发展、财政科技支出对区域风险投资具有显著促进作用，环境污染对风险投资有一定抑制作用。随着ESG投资理念逐渐获得认可，沿线地区可以通过提升绿色发展水平促进风险投资发展。     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Hedging and temporal permit issuances in cap-and-trade programs: The Market Stability Reserve under risk aversion

Cap-and-trade programs such as the European Union's Emissions Trading System (EU ETS) expose firms to considerable risks, to which the firms can respond with hedging. We develop an intertemporal stochastic equilibrium model to analyze the implications of hedging by risk-averse firms. We show that the resulting time-varying risk premium depends on the size of the permit bank. Applying the model to the EU ETS, we find that hedging can lead to a U-shaped price path, because prices initially fall due to negative risk premiums and then rise as the hedging demand declines. The Market Stability Reserve (MSR) reduces the permit bank and thus, increases the hedging value of the permits. This offers an explanation for the recent price hike, but also implies that prices may decline in the future due to more negative risk premiums. In addition, we find higher permit cancellations through the MSR than previous analyses, which do not account for hedging.     

政策不确定性、财政扩张与区域商业银行风险——基于中国地方官员变更和银行业的证据

文章利用2007-2017年我国93家区域商业银行的面板数据，并结合省级宏观经济数据和地方官员变更数据，实证考察了中国地方官员变更引起的经济政策不确定性对区域商业银行风险的重要影响。研究结果表明：（1）地方官员变更引起的政策不确定性增大了区域商业银行风险；（2）地方官员变更引起的政策不确定性，通过提高银行的资产收益率降低了商业银行风险；而不确定性时期的财政扩张，通过降低银行资产收益率增大了商业银行风险；并且财政扩张的负面影响大于政策不确定性的正面影响；（3）各省的市委书记发生职位更替引起的政策不确定性对区域商业银行风险的影响更大。文章研究为新时代背景下的金融供给侧结构性改革、政府宏观经济政策的制定以及财政体制与金融体制之间的联系提供参考。     

国家科技创新能力时空差异性评价

科技创新规模扩张和科技创新效率提升是增强国家科技创新能力的两条关键途径。基于现有研究鲜有结合科技创新规模和科技创新效率开展国家科技创新能力评价的现状，采用因子分析法，系统筛选国家科技创新规模影响因素，并结合科技创新效率影响因素，完善国家科技创新能力评价指标体系，通过构建DEA Malmquist指数模型，评价不同时期国家科技创新效率变化指数，以及中国与全球10个国家科技创新能力的时空差异性。研究表明，R&D投入强度、R&D研究人员、专利申请量、科技期刊文章数、高科技产品出口额占制成品出口总额的百分比、科技创新效率是影响国家科技创新能力的关键指标；1991-2014年中国科技创新能力水平呈指数增长态势，有力提升了国家科技实力；全球10个国家科技创新能力水平除日本出现一定波动外，其余九国整体呈小幅递增态势。