Objective methods for graphical structural learning

Graphical models are used for expressing conditional independence relationships among variables by the means of graphs, whose structure is typically unknown and must be inferred by the data at hand. We propose a theoretically sound Objective Bayes procedure for graphical model selection. Our method is based on the Expected-Posterior Prior and on the Power-Expected-Posterior Prior. We use as input of the proposed methodology a default improper prior and suggest computationally efficient approximations of Bayes factors and posterior odds. In a variety of simulated scenarios with varying number of nodes and sample sizes, we show that our method is highly competitive with, or better than, current benchmarks. We also discuss an application to protein-signaling data, which wieldy confirms existing results in the scientific literature.     

Trading volume and stock returns: A meta-analysis

I examine 468 estimates on the relationship between trading volume and stock returns reported in 44 studies. I study publication bias together with Bayesian and frequentist model averaging to explain the heterogeneity in the estimates. The results yield three key conclusions. First, publication bias distorts the findings of the primary studies. Second, the predictability of stock returns varies with different markets and stock types. Third, different data characteristics, structural variations and methodologies used drive the heterogeneity in the results of the primary articles. In particular, one should be cautious when using monthly data or VAR models.     

Optimal model averaging forecasting in high-dimensional survival analysis

This article considers ultrahigh-dimensional forecasting problems with survival response variables. We propose a two-step model averaging procedure for improving the forecasting accuracy of the true conditional mean of a survival response variable. The first step is to construct a class of candidate models, each with low-dimensional covariates. For this, a feature screening procedure is developed to separate the active and inactive predictors through a marginal Buckley–James index, and to group covariates with a similar index size together to form regression models with survival response variables. The proposed screening method can select active predictors under covariate-dependent censoring, and enjoys sure screening consistency under mild regularity conditions. The second step is to find the optimal model weights for averaging by adapting a delete-one cross-validation criterion, without the standard constraint that the weights sum to one. The theoretical results show that the delete-one cross-validation criterion achieves the lowest possible forecasting loss asymptotically. Numerical studies demonstrate the superior performance of the proposed variable screening and model averaging procedures over existing methods.     

金融资源配置、技术进步与经济高质量发展

随着我国发展阶段及内外部条件的变化,科技创新已成为新时代引领我国经济高质量发展的核心驱动力。本文以金融驱动技术进步的视角,构建内生增长DSGE模型,分析金融资源配置、技术进步和经济增长之间的关系。研究佐证了我国技术创新的逆周期性,并发现金融资源在企业生产性投资与创新投资之间的分配,可以通过改变生产要素投入规模和技术进步率两种途径影响经济增长,且这两种影响之间存在“跷跷板”关系,但后者更具主导性。在此基础上,诠释了金融、技术与经济的动态传导机制:经济扩张期,企业生产规模扩张,金融资源对生产性投资的支持增加,对创新的投入相对减少,技术进步率放缓;经济收缩期,企业缩减生产规模,金融资源对生产性投资的支持减少,对创新的投入相对增加,技术进步率提升。本研究的政策启示为,面对当前发展新矛盾、新挑战,应把握发展新格局、新机遇,进一步深化金融改革,优化金融资源配置机制,鼓励创新投资,充分发挥股权市场对企业研发创新的支持作用,并辅以稳健适度的宏观调控政策,为企业创新提供良性的宏观环境,激活企业创新需求,促进技术进步,推动经济高质量发展。     

Exploring the development trend of internet finance in China: Perspective from club convergence

We first employ β-conditional convergence and log t regression tests based on nonlinear time-varying factor model and club clustering algorithm to analyze the convergence characteristics of the development level of Internet finance in 335 prefecture-level cities in China. The result of log t regression test illustrates that there is no convergence as a whole in the development level of China's Internet finance. However, seven convergence clubs and a divergent group have been formed, and the development level and growth rate of Internet finance among these convergence clubs have shown obvious differences. Moreover, we also employ the Ordered Probit to explore the formation mechanism of the convergence clubs. The results reveal that the regions with a higher level of economic development, traditional financial development, economic openness and Internet development are more inclined to converge in a club with a higher Internet finance development level. Alternatively, the regions that are interfered with more by the government or that have a lower degree of marketization, tend to converge in a club with a lower level. Finally, according to the conclusions, we propose corresponding policy recommendations for promoting the regional coordinated development of China's Internet finance.     

高新技术企业间知识治理对协同创新绩效的影响

知识治理是知识管理理论发展的新阶段，在动态复杂的创新环境下，知识治理与创新的关系已成为学术界关注的热点话题。对高新技术企业进行问卷调查，探讨知识治理对协同创新绩效的影响并采用有调节的中介效应模型，分析知识转移和制度环境在其中的作用机制。结果表明，正式知识治理与非正式知识治理均会对知识转移产生正向影响；知识转移在知识治理与协同创新绩效关系中存在完全中介效应，即知识治理对协同创新绩效的正向效应是通过知识转移实现的；制度环境正向调节知识转移对协同创新绩效的影响，即在良好的制度环境下，知识转移的中介作用更强。     

预期寿命、生育率变动与基本养老保险统筹账户调整

在构建三期世代交叠模型的基础上，通过选取相关制度指标来模拟分析在未来预期寿命和生育率变动下，基本养老保险统筹账户如何基于参数调整来维持收支平衡。研究结果表明：（1）我国居民预期寿命增长过快，统筹账户收支平衡受预期寿命变化影响较大；（2）在现阶段的预期寿命和生育率条件下，统筹账户能实现自身收支平衡，而且可在短期内通过阶段性降低缴费率来促进制度长远发展；（3）在长期内需通过提高缴费率或降低养老金替代率来保证统筹账户收支平衡，预期寿命为87.828岁且总和生育率为1.79作为参数调整临界点；（4）以现有制度设计为基准，参数调整临界点之前保证35%的基础养老金平均替代率不降，临界点之后保证20%的缴费率不升可以最大程度促进经济和社会发展。     

城市轨道交通相邻多车站动态协同限流研究

为应对城市轨道交通车站高峰期客流压力,限流已经成为常态应对措施,而经常采用的限流策略大多针对单个车站,缺少对相邻多个车站的协同考虑。基于客流在车站间的传播效应,以乘客总延误时间最小为目标,以站台及列车能力为约束条件,构建城市轨道交通相邻多车站协同限流决策模型,同时为实现实时动态控制,引入滚动时域控制方法,优化多车站动态协同限流流程。以北京地铁8号线为例,经过相邻若干车站运营数据进行限流策略计算。结果表明,该模型限流可以在不造成车站服务人数损失的同时,将乘客总延误时间降低21%。     

What is happening in Paris? Airbnb,hotels, and the Parisian market: A case study

Airbnb has been the subject of heated discussions among tourism and hospitality scholars. In order to understand the true impact of P2Psharing on the tourism and hospitality landscape, it is important to understand that market in detail first. Therefore, in this paper, we explore the development of the Airbnb market over the last seven years in Paris, the most popular destination for Airbnb guests with more than 40,000 accommodation rentals. This research note is a summary of our findings on the Paris market. Our study shows different growth and seasonality patterns for Airbnb and hotels and dissimilarities in the geographical location of the offers. The findings indicate that the two products are not in direct competition and that their relationship might be more complex than previously thought. We trust that exploring the nature of the competitive relationship would be a valuable avenue for further investigation.     

开放式创新社区中用户交互反馈对个体创新贡献度的影响

开放社区中的用户交互反馈是用户创新活动的重要组成部分，基于需求满足理论，研究其他用户反馈对个体创新贡献度的影响。结合典型开放式产品设计社区——Local Motors平台，采用数据挖掘方法，从数量、质量和情感3个方面研究开放式创新社区中FOU对个体创新贡献度的影响。结果表明，反馈数量和质量均对个体创新贡献度有显著影响，其中反馈次数对个体创新贡献度有显著正向影响，反馈率和反馈人员多样性对个体创新贡献度有显著负向影响，反馈长度和反馈支持文件对个体创新贡献度没有影响，正向情感倾向在用户反馈和个体创新贡献度之间无任何调节作用。