创新要素集聚、政府支持与科技创新效率——基于省域数据的空间面板计量分析

本文运用空间面板计量方法研究了创新要素集聚、政府支持与科技创新效率之间的关系。实证研究的结果表明:我国科技创新效率具有明显的空间相关性。创新要素集聚对于科研机构创新效率具有负影响,对于高校的影响不显著,而对于企业的影响为正。政府支持对于科研机构和高校创新效率的影响具有不确定性,而对于企业具有负影响。制度因素对于科研机构和高校创新效率没有明显影响,而对于企业具有明显的正影响。人力资本对于高校和企业创新效率具有正影响。FDI因素对于企业创新效率具有正的外溢效应。因此,政府需要通过对人力资本的知识和健康投资,提高人力资本的质量,才能实现更快的创新产出和经济增长。     

江苏省区域物流业发展水平的比较分析

近年来,江苏省现代物流业整体得到了快速发展,但区域物流发展水平不平衡现象较为明显。本文采用多元统计分析的因子分析和聚类分析对江苏省13个地级市物流业发展水平进行了比较分析,从中找出各地区自身的优劣势以及地区间的差距,最后就提升江苏省区域物流业发展水平提出了相应的对策建议。     

论税收对要素收入分配的影响

我国自1994年分税制改革以来,一方面税收收入保持高速增长,另一方面国民收入分配格局发生显著变化——劳动要素分配份额迅速下滑,资本要素分配份额逐渐上升。本文的理论分析表明,税收通过替代效应和收入效应影响要素收入分配,前者改变了生产中要素相对投入比例,从而改变了税前要素收益率,后者则是通过直接税影响到税后要素收益率。本文利用系统GMM估计进行的实证分析表明,我国税收对要素收入分配具有明显影响:就直接税而言,企业所得税降低了资本分配份额,个人所得税中对劳动征税部分降低了劳动分配份额;就间接税而言,增值税明显降低劳动分配份额但对资本分配份额的影响不明显,营业税明显降低资本分配份额而对劳动分配份额的影响不明显。因此,如果从有利于调整要素收入分配格局角度考虑,我国有必要进一步调整现行税收政策、完善税收制度。     

政府公共卫生支出行为影响因素的实证分析

公共卫生支出属于财政支出的重要组成部分。随着我国公共财政体制的建立,合理界定公共卫生支出占财政支出的比重显得尤为重要。本文使用31省、直辖市、自治区2000—2010年的面板数据,实证分析了我国地方政府公共卫生支出的影响因素。研究表明,人口规模、人口结构、城市化程度与政府公共卫生支出并没有必然的因果关系;经济发展水平和政府机构及人员的膨胀程度与政府公共卫生支出之间存在着正相关关系;财政分权也只是对公共卫生支出产生很小的正面影响。     

贵州省工业全要素生产率的测算：1979—2009

基于索洛余值法并采用柯布一道格拉斯生产函数测算贵州省1979--2009年期间工业全要素生产率增长率,并简要分析贵州省工业全要素生产率增长和贵州工业增长源泉。分析表明：贵州工业增长的走势与贵州工业TFP增长波动相一致,可见较高的工业TFP增长率对贵州工业经济的增长具有巨大的推动作用。     

区域技术创新及其影响因素的空间计量分析

利用空间统计的Moran指数和空间截面数据模型,对影响我国区域技术创新的因素进行计量分析。结果表明：区域创新环境对我国区域技术创新为显著的负效应;大学和创新企业对我国区域技术创新有显著的促进作用;研究机构对我国区域技术创新没有显著的贡献;创新企业对提高技术创新能力的贡献最大。     

江、浙、沪商务成本构成比对分析及对产业转移路径的影响

本文对江、浙、沪商务成本构成趋势进行比对,并探讨其对产业转移路径的影响。研究表明:一般而言,随着经济发展,要素成本不断上升,交易成本逐步下降,而商务成本总体呈现上升的趋势。商务成本构成变化以及总体上升的趋势会对产业转移路径产生影响,大多处于产业生命周期成熟阶段的行业和劳动密集型行业向要素成本更低区域产业转移,处于产业生命周期创新和成长阶段的新兴行业则向交易成本更低区域产业集聚。经过比对,发现要素成本上海最高,江苏最低;上海交易成本最低,粗略认为浙江交易成本略低于江苏,江、浙、沪要素成本与交易成本的差异又使得制造业与第三产业部分行业在江浙沪区域内进行转移和集聚。     

The economic benefits of market timing the style allocation of characteristic-based portfolios

Many exchange traded funds track simple characteristic-based equity portfolios such as the market capitalization, the fundamental value or the inverse volatility portfolio. This paper provides theoretical and empirical evidence for the economic benefits in exploiting the timing-gains that result from the time-varying relative performance of these characteristic-based portfolios. Under a factor model for expected returns, we show that this dynamic portfolio allocation can be efficient across the low-dimensional set of characteristic-based portfolios. We assess the out-of-sample performance on the S&P 100 universe over the period 1990–2013 and show gains in stability and significant positive risk-adjusted returns for the dynamic style portfolio. We conduct several robustness tests and extensions confirming the benefits of dynamic style allocation across characteristic-based portfolios.     

Electricity price forecasting: A review of the state-of-the-art with a look into the future

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures, and (iii) statistical testing of the significance of one model’s outperformance of another.     

Insurance Considering a New Stochastic Model for the Discount Factor

In many empirical situations (e.g.: Libor), the rate of interest will remain fixed at a certain level (random instantaneous rate i i ) for a random period of time ( t i ) until a new random rate should be considered, i i + 1 , that will remain for t i + 1 , waiting time until the next change in the rate of interest. Three models were developed using the approach cited above for random rate of interest and random waiting times between changes in the rate of interest. Using easy integral transforms (Laplace & Fourier) we will be able to calculate the moments of the probability function of the discount factor, V ( t ), and even its c.d.f. The approach will also be extended to the calculation of the expected value (net premium) and variance of a term insurance and we will get its c.d.f., something not very common in actuarial literature due to its complexity, but very useful when the law of large numbers cannot be applied and consequently use normal approximations.