贸易摩擦背景下人民币中亚区域化研究——基于能源合作推动路径分析

在中美贸易摩擦不断升级和"去美元化"的趋势下,人民币国际化迎来了新的机遇与挑战.当前背景下人民币计价与结算功能偏离、人民币金融交易功能的缺失是人民币国际化进程的主要绊脚石.能源兼备物质与金融属性,在弥补人民币定价与金融交易功能中发挥着重要作用.文章基于中美贸易摩擦对人民币国际化的影响,探讨能源合作对实现人民币国际化的战略意义,通过数据分析中国与中亚间能源合作现状并以此为立足点,提出了提高跨境能源贸易人民币结算额、扩大人民币对外投资额、建设中哈霍尔果斯离岸人民币金融市场等以能源合作推动人民币国际化的具体路径.     

全国煤炭交易中心运营模式研究

全国煤炭交易中心的设立对规范我国煤炭交易市场规则、实施能源宏观调控、提升我国煤炭国际定价话语权具有重要意义。在分析全国煤炭交易中心功能定位和业务的基础上,设计中长期合同邀约、现货挂牌、现货竞价、现货招投标4种交易模式及业务流程,提出依托国家重大战略争取政策支持、加强各方沟通完善综合物流体系、建立银企合作机制与信用体系、完善煤炭交易中心协调机制、增强信息服务与风险防控能力等对策建议。研究成果对优化煤炭供给结构、规范煤炭交易市场和保障国家煤炭能源安全提供了支撑。     

浅析城市轨道交通桥梁抗震设计

城市轨道交通当前已经成为国内各大城市的主要交通工具之一,其抗震性能对于当地居民的人身安全、交通秩序以及经济发展效益都有着极为关键的战略含义.在地震灾害下,怎么确保城市轨道交通系统的结构稳定、行驶安全是城市轨道交通系统建设工作的关键性难题,为此,本文对其抗震设计中的一些关键问题开展相关论述,希望能够为相关人员提供参考.     

产业升级、金融分权与民营企业融资约束

本文在产业升级的框架下,分析了国有企业对民营企业存在的信贷挤入与挤出效应.产业升级过慢或过快都将使得信贷挤出效应占主导,金融分权(Ⅰ、Ⅱ)是产业升级影响民企融资约束的重要金融渠道,基于2007-2016年中小板上市公司数据和省级宏观数据,主要通过现金-现金流敏感性模型进行了实证检验.结果表明:(1)相对于产业升级速度适中地区,过慢和过快地区的民营中小企业融资约束更为严重;(2)国企产业升级通过决定金融分权影响民企融资约束,金融分权Ⅱ本身能缓解民企融资约束,但国企产业升级过快会降低金融分权Ⅱ,同时金融分权Ⅰ会刺激地方政府过度投资反而会加剧民企融资约束;(3)金融分权对民企融资约束的影响受到财政分权的显著调节作用,地方财政越宽松,金融分权的作用越显著;(4)对产业升级过慢地区,在金融措施配合下,充分发掘国企产业升级先导性作用是缓解民企融资约束的有效途径.     

科技型中小企业创新生态系统构建机制研究

文章在阐述科技型中小企业创新生态系统概念及构成要素的基础上,提出了由动力机制、知识转移与共享机制、利益协调机制和外部治理机制组成的科技型中小企业创新生态系统构建机制理论框架.通过对长三角地区200家科技型中小企业的问卷调查,分析了上述四大机制的影响作用,并根据调查结果进一步提出,要有效弥补科技型中小企业创新资源不足、打造线上线下相结合的知识共享平台、建立合理的利益分配机制、加强政府对创新生态系统建立的支持与引导、建立更加专业的科技中介服务体系,从而推进并完善科技型中小企业创新生态系统构建.     

Fiscal Stimulus and Endogenous Firm Entry in a Monopolistic Competition Macroeconomic Model

This paper sets up a monopolistic competition model featuring the returns to production specialization. Some novel results are derived from the analysis. First, the effect of a fiscal stimulus on consumption may be positive or negative, depending crucially upon whether the production function is characterized by increasing or decreasing returns to production specialization. Second, following a fiscal expansion, increasing returns to specialization lead to a positive linkage between real wages and aggregate output, while decreasing returns to specialization result in a negative relationship between real wages and aggregate output. Third, a fiscal expansion may raise social welfare, provided that the degree of increasing returns to production specialization is sufficiently large.     

Household Determinants of the Adoption of Improved Cassava Varieties using DNA Fingerprinting to Identify Varieties in Farmer Fields: A Case Study in Colombia

We examine factors affecting the adoption of improved cassava varieties of 217 households in the Cauca Department in southwest Colombia. Using DNA fingerprinting through Single Nucleotide Polymorphisms (SNPs), we identified different cultivars in farmers fields. We also used this information to remove possible bias in the adoption model that could have resulted from a misclassification of improved varieties (IVs). As a result, we found that farmers substantially overestimate their use of IVs and there are important differences in the determinants of adoption between farmer self‐identification and DNA fingerprinting. This finding implies that the incorporation of DNA fingerprinting in IV adoption studies is important to ensure the accuracy of future agricultural economic research and the relevance of subsequent policy recommendations.     

Building relationship innovation in global collaborative partnerships: big data analytics and traditional organizational powers

This study examines how relationship innovation can be developed in global collaborative partnerships (alliances, joint ventures, mergers, and acquisitions). The recently emerging theory of big data analytics linked with traditional organizational powers has attracted a growing interest, but surprisingly little research has been devoted to this important and complex topic. Therefore, after developing the theoretical foundations, our study empirically quantifies the links between the theoretical constructs based on the data collected from chief executive officers, managing directors, and heads of departments who work in contemporary global data‐and‐information driven collaborative partnerships. The results from structural equation modeling indicate that the relationship innovation depends on the power of big data analytics and non‐mediated powers (NMP, expert and referent). The power of big data analytics also mediates the correlation between NMP and relationship innovation. However, mediated powers (coercive and manipulative) negatively affect the power of big data analytics and relationship innovation. The interaction effects further depict that analytically powered partnerships have better relationship innovation compared with those which focus less on the analytical power. Consequently, the contributions of this study provide a deeper understanding of mechanisms of how modern collaborative partnerships can use big data analytics and traditional organizational powers to co‐create relationship innovation.     

Target‐Setting,Pay for Performance,and Quality Improvement: A Case Study of Ontario Hospitals’ Quality‐Improvement Plans

This study examines whether difficult targets and quality indicators in executives' pay‐for‐performance (P4P) plans affect performance. The impact of target‐setting and P4P plans on quality improvement in the public sector is unclear. The Ontario government initiated the Quality Improvement Plan (QIP), which requires hospitals to set targets for quality indicators annually and link executive pay to target achievement since 2011. Analyzing Health Quality Ontario's database and hospitals' 2012–2013 QIPs, this study shows greater quality improvement in hospitals with difficult targets than hospitals with easy targets or without assigned targets; however, the positive impact disappears for high‐performance hospitals relative to their peers. We find no significant effect of the use of quality indicators in executives' P4P plans on quality improvement. Copyright © 2018 ASAC. Published by John Wiley & Sons, Ltd.     

Time series copulas for heteroskedastic data

We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first‐order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co‐movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value‐at‐risk forecasts.