中国高端装备制造业自主技术创新效率稳定性及影响因素研究

在全面实施创新驱动发展战略的时代背景下，揭示中国高端装备制造业自主技术创新效率稳定性，测度相关因素的影响强度与方向，对揭示中国高端装备制造业自主技术创新规律，制定其发展策略具有重要指导意义。聚焦中国高端装备制造业自主技术创新效率稳定性及影响因素，采用DEA-Malmquist指数方法和Tobit回归模型进行实证研究，结果表明，中国高端装备制造业自主技术创新效率存在一定波动性，且各细分行业技术创新效率内部稳定性较差，产业自主技术创新效率整体稳定性不高；自主技术创新投入因素和环境影响因素对产业自主技术创新效率的影响具有较高的异质性，其中创新资金投入和人力资源投入对自主技术创新综合效率、纯技术效率及规模效率具有正向影响，而新产品开发投入和企业规模对自主技术创新综合效率、纯技术效率和规模效率的影响具有异质性，政策因素与市场因素分别具有显著和非显著负向影响，外源性创新因素具有显著正向影响，且影响强度较大。在此基础上，提出调整政府对高端装备制造业创新支持模式、加强外源性创新与智力引进等策略建议。     

税收优惠政策与高技术产业创新效率

研究目标：分析税收优惠政策对创新效率的影响以及创新效率的收敛性，测度税收优惠政策对创新带来的实际贡献。研究方法：构建随机前沿模型测度高技术产业的创新效率，采用σ收敛和β收敛检验方法检验创新效率收敛性，并采用反事实计量方法测度税收优惠政策对创新带来的实际贡献。研究发现：税收优惠政策对研发效率有显著正影响，对市场转化效率的影响为正但不显著，政策实施后效率分布也更集中；研发效率的收敛主要是由东中西地区内部之间差距的缩小造成的，而市场转化效率的收敛是由三大地区内部差距的缩小以及中西部和东部地区的差距缩小共同造成的；税收优惠政策使研发效率大约上升了8%～10%，带来的创新产出占总产出的比重也一直维持在10%以上。研究创新：从效率水平和收敛性两个角度来定量分析税收优惠政策对创新效率的影响。研究价值：为更好地采取措施提高创新效率和减小地区创新效率差距提供经验证据。     

Collaborative cost-cutting: productive efficiency as an interdependency between public organizations

Collaboration between public sector organizations is typically understood as a response to complexity. Agencies collaborate in order to address complex, cross-cutting policy needs that cannot be met individually. However, when organizational size is a constraining factor in public service efficiency, collaboration can also reduce costs by capturing scale economies unavailable to organizations of sub-optimal size. Using organization theory, the article conceptualizes these two different triggers for public sector collaboration, and builds a framework for tracing their wider impact upon the formation, operation, and outcome of inter-agency partnerships. The framework is illustrated, and its implications for future research are explored.     

我国高技术产业创新效率的制度贡献及其异质性

高技术产业创新为我国经济转型发展提供新动能，但其现存的低效率创新困局亟待破解。利用1997-2011年高技术产业面板数据，全面考察制度因素对高技术产业技术创新的影响，并就各个制度子因素对技术创新三阶段效率的贡献加以分析。结果显示，整体制度环境优化有利于创新效率提升，制度因素对不同阶段创新效率的贡献具有异质性；以企业为主体的科技创新体制对高技术产业创新效率的影响不显著，表明以企业为主体的创新体系有待进一步完善。最后提出继续深化市场化改革、优化整体制度环境、明确政府职能、真正确立企业创新主体地位等对策建议。     

Is energy consumption sensitive to foreign capital inflows and currency devaluation in Pakistan?

This study investigates the relationship between foreign capital inflows and energy consumption by incorporating economic growth, exports and currency devaluation in energy demand function for the case of Pakistan. The long-run and short-run effects are examined via ARDL bounds testing procedure. Foreign capital inflows and currency devaluation (economic growth and exports) decrease (increase) energy consumption in long-run. The results confirm a feedback effect between foreign capital inflows and energy consumption. These findings would be helpful to policy makers in designing comprehensive economic and energy policies for utilizing foreign capital inflows as a tool for optimal use of energy sources to enhance economic development in long run.     

Persistence of Price–Cost Margin and Technical Efficiency in the Indonesian Food and Beverage Industry

Abstract

This research investigates the persistence of price–cost margin (PCM) and technical efficiency (TE) of firms and the relation between these two factors in 44 subsectors of the Indonesian food and beverage industry in the period 1980–2014. Data envelopment analysis with a bootstrapping approach is applied to estimate TE. An autoregressive model, accounting for endogeneity, is applied to estimate the persistence coefficients of PCM and TE. A cross-sectional regression model is also applied to estimate the relation between the persistence of the PCM and the persistence of TE. The results show that for these food and beverage firms, high PCM and low TE persist. Furthermore, the persistence of PCM negatively affects the persistence of TE in the industry.     

企业突破性技术持续采用意愿的驱动机制研究

整合组织动机和期望确认理论，从购买动机视角探讨企业突破性技术持续采用意愿的驱动机制。将购买动机分为效率动机、规范趋同动机和模仿趋同动机，阐述购买动机对突破性技术持续采用意愿影响的微观机理。以198家购买过云计算技术的企业为研究样本，实证检验购买动机与突破性技术持续采用意愿间的关系，并检验满意和认知锁定的中介效应。结果表明：购买动机正向影响突破性技术持续采用意愿，满意和认知锁定在购买动机与突破性技术持续采用意愿之间起中介作用，各购买动机的中介作用路径呈现差异化。其中，效率动机的作用路径被满意和认知锁定部分中介，模仿趋同动机的作用路径被满意部分中介，规范趋同动机的作用路径被认知锁定部分中介。研究结论打开了企业突破性技术持续采用意愿的“黑箱”，对于企业深入理解突破性技术持续采用意愿的驱动机制，促进突破性技术在持续采用过程中发挥真正价值具有重要借鉴意义。     

Asset pricing and energy consumption risk

This paper proposes energy consumption in the US as a new measure for the consumption capital asset pricing model. We find that (i) industrial energy growth produces reasonable values for the relative risk aversion coefficient and the implied risk-free rate; (ii) compared to alternative consumption measures, industrial energy performs well in explaining the cross-sectional variation in stock returns with the lowest implied risk aversion and pricing errors; (iii) the industrial energy consumption risk model performs equally well as the Fama–French three-factor model in the cross-sectional asset pricing tests; and (iv) total energy consumption risk is priced in the presence of the Fama–French factor risks.     

Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting

The Global Energy Forecasting Competition 2017 (GEFCom2017) attracted more than 300 students and professionals from over 30 countries for solving hierarchical probabilistic load forecasting problems. Of the series of global energy forecasting competitions that have been held, GEFCom2017 is the most challenging one to date: the first one to have a qualifying match, the first one to use hierarchical data with more than two levels, the first one to allow the usage of external data sources, the first one to ask for real-time ex-ante forecasts, and the longest one. This paper introduces the qualifying and final matches of GEFCom2017, summarizes the top-ranked methods, publishes the data used in the competition, and presents several reflections on the competition series and a vision for future energy forecasting competitions.     

A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.