企业经济责任投入对财务绩效影响的实证研究

将企业经济责任分为对股东的责任、对债权人的责任、对供应商的责任、对员工的责任,以此来设定经济责任投入的变量,分析研究其对企业财务绩效的影响。选取2011~2012年中国最受尊敬企业中在沪、深交易所上市的15家企业作为样本,根据各企业发布的相关财务数据,采用计量经济模型的方法,对财务绩效进行回归分析。结果显示：债权人、供应商、员工的责任与财务绩效均呈显著正向关系,说明企业经济责任的投人会促进财务绩效的提高。最后,提出促进企业财务绩效增长的对策建议。     

安徽省城镇居民消费情况实证分析

文章根据《安徽省统计年鉴》中的相关数据 ,运用时间序列线性回归等模型对 1 995 -1 999年安徽省城镇居民的消费支出情况进行实证分析 ,以揭示近几年来安徽省城镇居民的消费支出的结构及特点 ,以及消费不旺局面的内在原因。同时文章还探讨了一些与消费密切相关的经济热点问题     

福建省科技资源配置效率影响因素分析

针对前期研究成果所测算出的值,从结构和社会环境两方面选取解释变量,运用多元回归分析法,对影响福建省科技资源配置效率水平的因素进行定量分析,研究确定了主要影响因素,为福建省制定合理的科技资源配置政策提供参考依据。     

M-quantile models with application to poverty mapping

Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately, cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation, using only the survey data, is inappropriate since it yields estimates with unacceptable levels of precision. Small area models are designed to tackle the small sample size problem. The most popular class of models for small area estimation is random effects models that include random area effects to account for between area variations. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier robust inference. An alternative approach to small area estimation that is based on the use of M-quantile models was recently proposed by Chambers and Tzavidis (Biometrika 93(2):255–268, 2006) and Tzavidis and Chambers (Robust prediction of small area means and distributions. Working paper, 2007). Unlike traditional random effects models, M-quantile models do not depend on strong distributional assumption and automatically provide outlier robust inference. In this paper we illustrate for the first time how M-quantile models can be practically employed for deriving small area estimates of poverty and inequality. The methodology we propose improves the traditional poverty mapping methods in the following ways: (a) it enables the estimation of the distribution function of the study variable within the small area of interest both under an M-quantile and a random effects model, (b) it provides analytical, instead of empirical, estimation of the mean squared error of the M-quantile small area mean estimates and (c) it employs a robust to outliers estimation method. The methodology is applied to data from the 2002 Living Standards Measurement Survey (LSMS) in Albania for estimating (a) district level estimates of the incidence of poverty in Albania, (b) district level inequality measures and (c) the distribution function of household per-capita consumption expenditure in each district. Small area estimates of poverty and inequality show that the poorest Albanian districts are in the mountainous regions (north and north east) with the wealthiest districts, which are also linked with high levels of inequality, in the coastal (south west) and southern part of country. We discuss the practical advantages of our methodology and note the consistency of our results with results from previous studies. We further demonstrate the usefulness of the M-quantile estimation framework through design-based simulations based on two realistic survey data sets containing small area information and show that the M-quantile approach may be preferable when the aim is to estimate the small area distribution function.     

基于顾客满意度陷阱的市场细分方法研究

提出了基于顾客满意度陷阱的市场细分方法,企业可以根据该细分结果对不同满意程度的顾客采取不同的营销战略,将投向满意顾客的部分资源转向不满意顾客和非常满意顾客,能够更有效地提升顾客忠诚度,并拉动企业的业绩表现。以某个电视制造企业为例,采用了聚类分析、主成分回归分析、多重对应分析等统计分析方法,验证了顾客满意度陷阱的存在,同时表明顾客的人口统计特征与顾客满意作用于顾客忠诚的方式存在对应关系,从而佐证了方法的合理性。     

ADE-4: a multivariate analysis and graphical display software

We present ADE-4, a multivariate analysis and graphical display software. Multivariate analysis methods available in ADE-4 include usual one-table methods like principal component analysis and correspondence analysis, spatial data analysis methods (using a total variance decomposition into local and global components, analogous to Moran and Geary indices), discriminant analysis and within/between groups analyses, many linear regression methods including lowess and polynomial regression, multiple and PLS (partial least squares) regression and orthogonal regression (principal component regression), projection methods like principal component analysis on instrumental variables, canonical correspondence analysis and many other variants, coinertia analysis and the RLQ method, and several three-way table (k-table) analysis methods. Graphical display techniques include an automatic collection of elementary graphics corresponding to groups of rows or to columns in the data table, thus providing a very efficient way for automatic k-table graphics and geographical mapping options. A dynamic graphic module allows interactive operations like searching, zooming, selection of points, and display of data values on factor maps. The user interface is simple and homogeneous among all the programs; this contributes to making the use of ADE-4 very easy for non- specialists in statistics, data analysis or computer science.     

信贷规模与房地产价格的非线性动态关系——基于中国直辖市数据的比较研究

运用非线性平滑转换模型对中国直辖市的信贷规模与房地产价格的动态关系进行对比研究。研究发现如果想要通过控制信贷规模调控房地产价格,天津和重庆更容易达成调控目标,北京则很难。在信贷规模对房地产价格产生非线性影响的条件下,信贷规模对房地产价格的影响存在不同状态,重庆在不同状态间的变化速度最快,其次是天津,最慢的是北京。     

基于时空地理加权回归模型的中国碳排放驱动因素实证研究

基于2006-2011年中国省级区域面板数据,应用时空加权回归模型(GTWR)实证考察了各驱动因素对碳排放规模和碳排放强度影响的时空差异。研究结果表明:大部分解释变量的时空系数估计值显著,波动性较为稳定,符号与预期一致,各驱动因素及其外溢效应在不同区域存在较强的空间异质性,且表现出一定的空间梯度分布。若实现区域差异化碳减排,需要充分考虑空间异质性和外溢性。     

Transformation using ( x + 0.5) to stabilize the variance of populations

Transformation is required to achieve homo-scedasticity when we perform ANOVA to test the effect of factors on population abundance. The effectiveness of transformations decreases when the data contain zeros. Especially, the logarithmic transformation or the Box–Cox transformation is not applicable in such a case. For the logarithmic transformation, 1 is traditionally added to avoid such problems. However, there is no concrete foundation as to why 1 is added rather than other constants, such as 0.5 or 2, although the result of ANOVA is much influenced by the added constant. In this paper, I suggest that 0.5 is preferable to 1 as an added constant, because a discrete distribution defined in {0, 1, 2, . . .} is approximately described by a corresponding continuous distribution defined in (0, ≧) if we add 0.5. Numerical investigation confirms this prediction. Received: October 16, 1998 / Accepted: June 10, 1999     

非线性回归分析的一种有理逼近

利用有理插值逼近回归函数,把最小二乘法和插值技术结合起来,充分利用已知信息,达到最佳逼近,通过实例,获得了很好的效果。