科技服务业产出测算：指标确立、数据收集与未来预测

科技服务业产出测算是对科技服务业产出数据的统计与分析，加强科技服务业产出数据的规范统计和应用分析可为我国政府调整产业政策提供重要数据支撑。以科技服务业产出测算为研究对象，结合学者观点、国家标准及各级政府相关产业政策，提出将分支产业作为初始指标的横向统计思路，构建科技服务业产出测算指标体系。在数据收集方面，强调要做好“收”与“填”两个基本工作；在指标确立和数据收集基础上，进一步提出未来预测的两种方法，以此作为科技服务业产出数据预测的初步探索；提出应从统一数据统计标准、完善数据调查与公开制度、加强数据资源开发3个方面建立健全我国科技服务业产出数据测量与管理体制机制。     

基于均方差法的郑州市土地综合承载力评价

［目的］开展土地综合承载力评价是科学编制国土空间发展战略的基础。通过构建包含水土资源承载力、经济承载力和生态承载力3个维度土地综合承载力评价指标体系，定量刻画郑州市城市土地综合承载力的特征与轨迹。［方法］运用均方差法，选取2010—2016年郑州市城市土地综合承载力评价指标，分析其土地综合承载力特征。［结果］（1）总体上，郑州市城市土地综合承载力不断增强，水土资源承载力呈现先升—降—升的动态演变特征； 经济承载力持续增长，这源于郑州市土地经济效益的大幅提升； 生态承载力呈现波动性增强的演变特征。（2）郑州市城市土地综合承载力经历了较低—中等—较高发展历程，但土地综合承载力处于低层次的较高阶段，仍有较大提升空间。（3）为提高郑州市城市土地综合承载力，应加快“多规合一”规划的编制，科学划定城市增长边界和“三生”空间，转变城市发展理念，优化发展发展路径，提升城市土地综合承载力水平。［结论］郑州市城市土地综合承载力仍有较大提升空间，应加快建立土地综合承载力扩容的有效机制。     

新能源发电企业资产管理措施研究

新能源行业发展速度加快,其中新能源发电企业是现阶段该行业发展的重要组成部门,企业的管理模式和体制是保证行业稳定发展的关键因素.在新能源发电企业中拥有大量多种不同类型的资产,资产管理是影响企业经济效益提高的主要因素.基于此,本文分析了新能源发电企业的资产特点,探讨影响新能源发电企业资产管理的因素以及具体策略,以期可以为企业发展提供有效参考.     

智能制造有利于增加企业技术创新投入吗——基于智能制造试点的准自然实验

智能制造是我国制造业转型升级的主攻方向,而如何准确客观评价智能制造的实施效果成为亟待解决的问题.从技术创新投入视角,将智能制造试点政策实施视为一次准自然实验,采用2011-2019年制造业上市企业面板数据,通过PSM-DID方法研究智能制造对企业技术创新投入的影响.研究结果表明,智能制造显著影响企业技术创新投入,即通过提升客户集成度、供应商集成度进而提高企业技术创新投入,且促进作用仅发生在民营企业和非高新技术行业中.该发现扩展了现有研究外延,同时,为解决当前国际困局和促进智能制造的持续推广提供了重要实践价值.     

Impulse response function analysis of the impacts of land value-added tax policy on government performance

In this study, an epsilon-based network data envelopment analysis is employed to construct assessment mechanisms for government performance. Moreover, performance indicators of two dimensions of tax collection efficiency and financial effectiveness are measured. We propose a vector autoregression model in which all economic variables are regarded as dependent variables to address the disadvantages of traditional regression model. The conclusions are as follows: (a) measures of tax collection efficiency deteriorated, whereas those of financial effectiveness improved. (b) In an impulse response analysis of the model, an increase in government-published land values produced significantly increased tax collection efficiency.     

Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.     

Communication and Comovement: Evidence from Online Stock Forums

We study investor communication and stock comovement using a novel data set from an active online stock forum in China. We find substantial comovement among the returns of a stock and its “related stocks,” which are frequently discussed in the subforum dedicated to the given stock. Comovement is greater when the discussion of related stocks is more intensive. Further, the effect of communication on comovement is stronger for stocks associated with higher information uncertainty. Codiscussed stocks are more actively traded and experience more correlated trading. A trading strategy that exploits communication‐driven comovement generates abnormal returns. Our findings highlight the impact of investor communication on asset comovement.     

基于效应的随机灰规划模型和应用

为了解决同时含有随机因素和灰色因素的不确定规划问题,通过结合区间灰数所属区间两个端点的随机性,给出随机区间灰数和随机区间灰函数的定义,提出了随机灰规划模型。通过综合效应函数理论用随机变量期望值和方差综合量化表示灰数所属区间的两个端点值。应用该理论对综合量化后的两个端点值继续进行综合量化,从而将随机灰规划转化为确定型规划问题。应用遗传算法进行求解。通过综合效应函数的理念,综合随机变量的期望和方差,同时综合区间灰数的区间因素,将随机灰规划数学模型转化为确定型规划模型即基于效应的随机灰规划模型。通过选取不同的综合效应函数,得到了关于不同决策意识下的随机灰规划的最优解。这个方法可为决策者进行不确定决策提供参考。