In-Sample Inference and Forecasting in Misspecified Factor Models

This article considers in-sample prediction and out-of-sample forecasting in regressions with many exogenous predictors. We consider four dimension-reduction devices: principal components, ridge, Landweber Fridman, and partial least squares. We derive rates of convergence for two representative models: an ill-posed model and an approximate factor model. The theory is developed for a large cross-section and a large time-series. As all these methods depend on a tuning parameter to be selected, we also propose data-driven selection methods based on cross-validation and establish their optimality. Monte Carlo simulations and an empirical application to forecasting inflation and output growth in the U.S. show that data-reduction methods outperform conventional methods in several relevant settings, and might effectively guard against instabilities in predictors’ forecasting ability.     

Factor screening in nonregular two-level designs based on projection-based variable selection

In this paper, we focus on the problem of factor screening in nonregular two-level designs through gradually reducing the number of possible sets of active factors. We are particularly concerned with situations when three or four factors are active. Our proposed method works through examining fits of projection models, where variable selection techniques are used to reduce the number of terms. To examine the reliability of the methods in combination with such techniques, a panel of models consisting of three or four active factors with data generated from the 12-run and the 20-run Plackett–Burman (PB) design is used. The dependence of the procedure on the amount of noise, the number of active factors and the number of experimental factors is also investigated. For designs with few runs such as the 12-run PB design, variable selection should be done with care and default procedures in computer software may not be reliable to which we suggest improvements. A real example is included to show how we propose factor screening can be done in practice.     

Bayesian variable selection approach to a Bernstein polynomial regression model with stochastic constraints

This paper provides a Bayesian estimation procedure for monotone regression models incorporating the monotone trend constraint subject to uncertainty. For monotone regression modeling with stochastic restrictions, we propose a Bayesian Bernstein polynomial regression model using two-stage hierarchical prior distributions based on a family of rectangle-screened multivariate Gaussian distributions extended from the work of Gurtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]]. This approach reflects the uncertainty about the prior constraint, and thus proposes a regression model subject to monotone restriction with uncertainty. Based on the proposed model, we derive the posterior distributions for unknown parameters and present numerical schemes to generate posterior samples. We show the empirical performance of the proposed model based on synthetic data and real data applications and compare the performance to the Bernstein polynomial regression model of Curtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]] for the shape restriction with certainty. We illustrate the effectiveness of our proposed method that incorporates the uncertainty of the monotone trend and automatically adapts the regression function to the monotonicity, through empirical analysis with synthetic data and real data applications.     

信任危机、逆向选择与品牌重建——来自网贷之家的经验证据

以中国P2P行业信任危机为背景,借助网贷之家公布的数据,利用面板固定效应模型研究信任缺失情形下的逆向选择问题以及政府和行业协会重建品牌信任的效果,结果表明:P2P行业信任危机下的平台旨在通过建设品牌形象来吸引投资人与借款人的做法是徒劳的,信任缺失导致合规平台的诚信经营并不为公众所认可,长此以往这些平台很可能会退出市场,进而引发逆向选择问题;自上而下的制度建设在重建品牌信任中取得了可喜的成绩,有效缓解并逆转了P2P行业的逆向选择问题,且市场整治初见成效。此研究成果对于目前正在进行的P2P行业整顿具有十分现实的指导意义。     

区块链赋能灾疫治理的价值逻辑、实践路径及其伦理反思

近年来,作为新型网络底层架构技术的区块链已经开始应用于社会治理领域,为国家治理能力现代化提供了新的着力点。人类面对灾疫的实践活动既是社会非常状态下的治理活动,又是道德意志驱动的伦理实践。区块链技术因其具有多维价值特征与灾疫治理的伦理目标高度契合,有望为现有灾疫治理体系进行赋能。同时,作为一项新兴的技术手段和灾疫治理架构,区块链赋能灾疫治理的伦理风险在于其可能超越了治理活动的边界。因此,需要警惕区块链技术对灾疫治理可能存在的价值偏离。     

理论观照与实践反思:乡村振兴道路背景下产业融合探究

为从全球性现代化的视野观察中国现代化道路,通过在湖北西部（鄂西南、鄂西北）、渝东南、贵州（六盘水、遵义、黔东北等）等地烟草行业综合体建设进行调查,对城乡融合发展中的产业融合进行综合考察和研究。研究认为,中国的乡村振兴从“战略”提升为“道路”,表明中国式“两条腿走路”的三农现代化道路认知正式成形,乡村振兴道路在本质上讲是“农村就地现代化道路”,以农业为基础的农村就地现代化道路的关键是农村产业的融合发展;走乡村振兴道路,必须坚持政府是“主导”、乡村是“主场”、就地现代化是“主道”、产业融合发展是“主业”、内生发展是“主流”、三农工作队伍是“主体”、城乡等值发展是“主向”,再从土地流转关系、政策法规、主导产业培育、共享经济平台建设等方面,培育和推进乡村振兴中的产业融合工程。     

资源禀赋、电商认知与家庭农场主电子商务采纳行为

基于河北、江西和云南3省893个家庭农场调研数据,研究了资源禀赋、电商认知、政府扶持对家庭农场主电子商务采纳行为的影响机理。结果表明:资源禀赋中的家庭农场主受教育程度、电商培训和农产品特色明显程度对其电子商务行为采纳及采纳程度有显著正向影响;年龄、注册品牌、绿色或有机认证、产品深加工对采纳程度有显著正向影响;组织化和电商认知对电子商务采纳行为有显著正向影响;政府扶持对电子商务行为采纳具有显著正向影响,且对资源禀赋、电商认知 电子商务行为采纳及采纳程度关系中有正向调节效应。在此基础上,提出培育农产品电商品牌,提升农场主电商运营能力,创新电商经营模式和提高家庭农场组织化程度等建议。     

商业银行利率选择与信贷配给问题研究

目前我国经济高速发展,但融资难的问题一直阻碍中小企业的进一步发展,这是多方原因造成的.其中长期存在于信贷市场中的信贷配给(credit rationing)现象、银行利率选择问题一直为国内学术界所关注,但是对于形成信贷配给的银行与企业的决策过程关注得不多.笔者从传统的信贷市场均衡入手,着重分析了信贷市场的利率选择、最优利率形成原则以及信贷配给产生的原因,以期对我国进行宏观经济调整起到一定的借鉴作用.     

论社会文明建设与社会主义和谐社会构建

构建社会主义和谐社会是中国特色社会主义事业的伟大实践.我们必须加强物质文明建设,为构建社会主义和谐社会奠定经济基础;加强精神文明建设,为构建社会主义和谐社会创造精神动力;加强政治文明建设,为构建社会主义和谐社会保驾护航;加强生态文明建设,为构建社会主义和谐社会提供可持续保证.     

基于熵权法的财务危机预警指标选择研究

合理选择财务指标是财务危机预警研究中的重要内容,用定性和定量相结合的方法进行选择是科学有效的方法之一。首先根据初选原则对财务指标进行了初步选择,然后提出了一种基于熵权的变量选择方法,对初选的财务指标进行定量筛选,进行实证研究,最终确定了应用于财务危机预警的财务指标。