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。     

数字素养与农户收入差距:加剧还是缓解?——基于中西部地区的微观证据

数字经济时代,数字贫困问题对农户收入差距的影响引发新的关注。基于2021年中国农村经济与农村金融数据,采用再中心化响应函数(RIF)回归方法探究数字素养对农户收入差距的影响。研究发现:(1)数字素养显著扩大了农户收入差距,并具备明显的马太效应,高数字素养能够攫取更多数字红利,而低数字素养更容易陷入“数字贫困”状态,无法发挥出数字经济的增收效应。(2)应用数字素养会显著加剧农户收入差距,而通用数字素养对农户收入差距的影响并不显著。这种情况也得以印证,现阶段数字“接入鸿沟”已经得以解决,数字“应用鸿沟”差距逐渐凸显。(3)数字素养能够缩小农户财产性收入差距,但会显著扩大工资性收入差距与非农经营性收入差距。脱贫地区受到数字素养的马太效应更强,农户收入差距明显加剧。研究结论证实了我国农村内部存在明显的数字鸿沟,同时数字贫困问题加剧了农户收入差距。新发展阶段我国数字乡村战略的推进不仅要注重农村低收入群体以及弱势群体数字素养的培育,更要加快构建农村数字经济的包容性发展路径,助力农民农村共同富裕的实现。     

数字金融对农户气候适应性行为的影响

气候适应性技术采用率低下已成为制约农业可持续发展的重要因素,数字金融可能影响农户的气候适应性行为。基于河南、陕西、山西三省1 384份农户微观调查数据,运用内生转换回归模型构建反事实分析框架,实证分析数字金融使用对农户气候适应性行为的影响效应及其作用机制。研究发现:使用数字金融能显著促进农户气候适应性技术采纳行为,具体表现为在反事实假设下,使用数字金融的农户若未使用其气候适应性技术采纳程度将下降;未使用数字金融的农户如果使用了,其气候适应性技术采纳程度将上升。机制分析表明,数字金融能够提高借贷易得性与信息易得性,进而促进农户采纳气候适应性行为,农户对于金融包容性的认知能够正向增强数字金融对农户气候适应性行为的影响效应。异质性分析表明,数字金融使用对于资本型适应性行为影响的边际效应最大,在反事实假设下也表现为数字金融使用对农户资本型适应性行为提升效果最强;数字金融对农业收入占家庭收入比率较高、接受过培训的农户采纳气候适应性技术促进效应更高。     

饭店类上市公司财务危机预警研究

财务危机预警分析正在成为企业提高管理水平的重要工具。我国上市饭店企业数量减少,经济效益总体差强人意。企业出现危机能够通过财务信息反映出来,将逻辑回归预警模型引入饭店企业管理中,有助于企业建立相应的预警应对机制,及时采取措施,以减少或消除财务危机带给企业的危害,提高上市饭店企业的竞争力。     

具有遗憾值约束的鲁棒供应链网络设计模型研究

考虑不确定性环境,研究战略层次的供应链网络鲁棒设计问题,目标是设计参数发生摄动时,供应链性能能够保持稳健性。基于鲁棒解的定义,建立从上游供应商选择到下游设施选址-需求分配的供应链网络设计鲁棒优化模型;提出确定遗憾值限定系数上限和下限的方法,允许决策者调节鲁棒水平,选择多种供应链网络结构;通过模型分解与协调,设计了供应链节点配置的禁忌搜索算法。算例的计算结果表明了禁忌搜索算法具有良好的收敛特性,以及在处理大规模问题上的优越性;同时也反映了利用鲁棒优化模型进行供应链网络设计,可以有效规避投资风险。     

TEI@I方法论及其在外汇汇率预测中的应用

基于TE I@I方法论的理论框架,构建了一个基于TE I@I方法论的外汇汇率预测模型。在此模型中,传统的经济计量模型用于处理外汇汇率的主要趋势,人工神经网络技术用于分析外汇汇率的非线性,而文本挖掘和专家系统用于处理外汇市场中的突现性和不稳定性。最后,基于集成的思想,利用支持向量回归技术对上述3个部分进行非线性集成,从而获得一个更为精确的预测结果。通过实证方法验证了基于TE I@I方法论的外汇汇率预测模型的有效性。