精准扶贫政策对脱贫农户家庭消费的影响研究——来自湘鄂渝黔毗邻民族地区调研数据的例证

基于湘鄂渝黔毗邻民族地区调研数据,本文考察精准扶贫政策对脱贫农户家庭消费的影响。研究发现：(1)精准扶贫政策对湘鄂渝黔毗邻民族地区脱贫农户家庭消费具有促进作用。就消费结构而言,精准扶贫政策使脱贫农户家庭居住和交通通信支出占总消费的比例提高,而教育和医疗支出占比降低。就消费类型而言,精准扶贫政策对生存型消费和发展型消费均有提高作用,生存型消费作用突出。就扶贫政策而言,产业就业扶贫政策作用最大,而保险扶贫政策作用最小。通过分位数回归、安慰剂检验等方法检验,上述结论仍成立。(2)异质性结果表明,精准扶贫政策对不同地区、民族和收入的脱贫农户家庭消费有明显差别,其中对湖南省、少数民族和低收入脱贫农户的刺激作用最大。(3)进一步研究发现,对于因病致贫、因教致贫和脱贫边缘户这三类特殊家庭,他们通过健康扶贫、教育扶贫、产业就业扶贫等多元化的精准扶贫政策综合发力,家庭消费水平得以改善,家庭生活质量得到提升。本文旨在为巩固拓展脱贫攻坚成果与乡村振兴的有效衔接、促进少数民族农村地区可持续发展提供理论依据和实践支撑。     

基于SODM的贝叶斯分类器结构学习及其在客户分类中的应用

将自组织数据挖掘理论引入贝叶斯分类中,提出一种新颖的贝叶斯分类器结构学习算法.算法将基于依赖分析和评分搜索两种贝叶斯网络结构学习思想相接合,根据互信息测度值选择初始模型,用贝叶斯评分作为筛选中间模型的外准则,能够在不同数据集上完成自适应建模过程,包括选择进入模型的变量、确定具有最优复杂度的模型结构等.在 10 个UCI数据集上进行分类测试,结果表明,贝叶斯分类器结构学习算法分类器的分类精度要高于常用的朴素贝叶斯、树扩展朴素贝叶斯以及基于K2算法的分类器.进一步地,在信用卡客户分类数据集german上的学习曲线和抗干扰试验还表明,与朴素贝叶斯、树扩展朴素贝叶斯以及K2等分类器相比,贝叶斯分类器结构学习算法分类器具有更加稳定的分类性能和更强的抗干扰能力.     

健康、异质性家庭投资者与资产配置

家庭金融理论认为家庭成员健康状况对家庭金融资产配置有着重要影响,但基于微观数据的经验研究并没有获得支持这一观点的广泛证据.此类经验研究的一大困难也是争议之处在于健康具有的主观和客观的双重特征,如何合理度量健康程度成为关键性问题.另一方面,投资者的异质性也没有得到足够的重视,文献中经常使用以老年人为主的有偏数据,实际上不同年龄段、不同财富人群的健康冲击对资产配置的影响并不一致.使用医疗费用占人均财富的比例作为家庭健康状况的代理变量,用具有代表性的中国居民家庭微观调查数据,研究发现不同年龄阶段的健康冲击对家庭资产配置的影响是不同的,健康冲击的影响在不同财富群体之间也不一样.因此,应该继续加强医疗保障体系的深度,并且有针对性的对待不同群体的健康问题.     

基于GLM的贝叶斯变量与模型选择

针对非正态响应的部分因子试验,当筛选试验所涉及的因子数目较大时,提出了基于广义线性模型(generalized linear models,GLM)的贝叶斯变量与模型选择方法.首先,针对模型参数的不确定性,选择了经验贝叶斯先验.其次,在广义线性模型的线性预测器中对每个变量设置了二元变量指示器,并建立起变量指示器与模型指示器之间的转换关系.然后,利用变量指示器与模型指示器的后验概率来识别显著性因子与选择最佳模型.最后,以实际的工业案例说明此方法能够有效地识别非正态响应部分因子试验的显著性因子.     

基于CPFR的需求预测新方法——分位数回归预测法

本文引入计量经济学前沿预测研究方法-指数加权分位数回归预测,并建立需求预测模型.该模型通过直接预测销售序列的分位数,避免既存研究中基于假设的预测失误,使预测结果更加贴近需求模式的真实值.在此基础上建立基于CPFR的供应链系统成本模型,对比分析表明指数加权分位数回归方法的预测精度较高.     

石油期货收益率的分位数建模及其影响因素分析

石油期货收益率的分位数反映了收益率分布特征和石油市场风险特征,有必要建模考察分位数的变化模式与影响因素。针对现有研究在模型方法和分析角度上的不足,本文考虑分位数受市场冲击影响而产生的非线性自回归特征,提出门限CAViaR模型并用以分析石油期货收益率的分位数及其影响因素。基于1998-2009年布伦特原油期货价格的研究表明,石油期货收益率的分位数具有自回归特征并受前期油价涨跌的不对称影响,且油价下跌的作用更强。左尾分位数受油价涨跌的共同影响,而右尾分位数仅受油价下跌的影响,二者呈现不同特征。此外,本文通过考察分位数的动态变化模式揭示了油价风险特征,具有重要的风险管理作用。     

关系、保险购买与保险赔付

基于中国家庭金融调查2011年数据,研究了“关系”对家庭商业保险购买及保险赔付的影响.利用构建的家庭关系指标,研究发现:1)关系每增加1％,家庭购买商业保险的概率大约增加0.3％,家庭获得保险赔付的概率增加0.06％;2)不同维度的关系子指标对家庭购买商业保险的影响不同;3)关系可以显著提高家庭购买寿险、财产险的概率,但是对养老险、健康险没有显著影响;4)关系可以通过社会互动影响家庭购买保险和获得保险赔付的概率.     

保险保障、家庭资本增长与贫困陷阱

构建包含保险机制和不含保险机制的两个家庭资本随机增长模型,并给出家庭陷贫概率,以此研究保险保障在家庭摆脱重大突发事件引致的贫困陷阱中的作用.研究发现,家庭获得保险保障后的陷贫概率由可增长资本效应和保险补偿效应共同决定.相对富裕家庭购买保险能降低陷贫概率,且比例保险的赔付比例越高,陷贫概率越小.相对贫困家庭购买保险后的陷贫概率可能提高也可能降低,取决于临界资本对保费费率的敏感系数.无论是相对富裕家庭还是相对贫困家庭,随着保险公司附加保费系数的下降,两种家庭的陷贫概率都随之下降.     

大数据下分位数回归通讯有效算法及其应用

考虑风险度量中常见的分位数回归模型，给出在超大容量数据且复杂数据类型下的几类快速分布式算法.虽然仅考虑分位数回归模型，但本文提供的算法大多数可以应用到其它更一般的模型中.由于分位数回归模型的目标函数为非光滑函数，通常的分块集成法和光滑函数高效通讯算法并不适用.本文首先针对完整观测数据，给出了分位数回归模型参数估计的等度连续法，光滑函数逼近法和改进的数萃(Meta)方法三种分布式通讯有效算法.进一步，考虑了非平衡半监督数据，分别针对无标签数据样本量较小和较大两种情形，提出了加权损失函数法和改进的数萃方法两种数据融合方法.所提出的方法可以把分散在不同机器上的半监督数据进行数据融合，从而实现不同数据类型和不同样本量情形下的高效通讯分布式计算，提高算法的精度和参数估计的效率.本文通过大量仿真模拟研究了所提出的算法在有限样本下的表现，并将其应用到了洛杉矶流浪人口数的实际数据分析中，发现其均具有较好的准确性.     

基于动态面板数据分位数回归模型的福州市房价影响因素分析

房地产业作为基础性和先导性产业,对经济和社会的影响有着举足轻重的作用。因此,房价影响因素的探讨一直都是国内外的热点话题。该文在研究城市人口密度、家庭人均可支配收入以及利率对房价的影响机制的基础上,选取了福州市2010—2017年的样本数据,利用动态面板数据的分位数回归模型进行了实证分析。结果表明,家庭可支配人均收入和人口密度的增加对房价的正影响随着分位数的增加呈逐步上升的趋势,即福州房价上涨的主要因素是家庭人均可支配收入和城市人口密度,利率的变化并不是福州市房价变动的主导因素。     

Coordination of benefits reduces double payments.

As a means of reducing the cost of duplicate health care coverage, the health insurance industry utilizes a mechanism called coordination of benefits (COB). The main purposes of COB are to limit recovery to 100 percent of actual charges and to assign insurers primary and secondary responsibilities to pay these charges. Nearly all health plans, including HMOs, Blue Cross/Blue Shield plans, and commercial insurers, coordinate benefits, mostly for group coverage, often on the basis of procedures found in state insurance codes. While COB provides an effective cost reduction mechanism to health insurers, several issues remain in its administration, including difficulties that arise when carriers refuse to pay, when HMO members self-refer, and when coordination is attempted with an uninsured plan.     

我国金融机构的系统性金融风险评估——基于极端分位数回归技术的风险度量

本文将极值理论引入到系统性金融风险度量中,通过极端分位数回归技术估计我国33家上市金融机构对金融系统整体的风险贡献,并识别出我国系统重要性金融机构。研究结果表明,我国金融机构的市场价值总资产收益率呈现明显的非正态分布特征,使用极端分位数回归技术可以更准确的度量尾部的风险联动性;银行类金融机构的系统性风险贡献水平最高且波动变化最大,系统性风险贡献排名前十的金融机构基本为银行类机构;证券类、保险类、信托类金融机构的风险贡献水平相对较低;通过与其他研究的对比发现,考虑到极端情形下的尾部风险联动性时,股份制商业银行对金融系统的风险贡献上升。本文的研究为系统重要性金融机构的宏观审慎监管提供了实证依据。     

Risk Factor Selection in Rate Making: EM Adaptive LASSO for Zero‐Inflated Poisson Regression Models

Risk factor selection is very important in the insurance industry, which helps precise rate making and studying the features of high‐quality insureds. Zero‐inflated data are common in insurance, such as the claim frequency data, and zero‐inflation makes the selection of risk factors quite difficult. In this article, we propose a new risk factor selection approach, EM adaptive LASSO, for a zero‐inflated Poisson regression model, which combines the EM algorithm and adaptive LASSO penalty. Under some regularity conditions, we show that, with probability approaching 1, important factors are selected and the redundant factors are excluded. We investigate the finite sample performance of the proposed method through a simulation study and the analysis of car insurance data from SAS Enterprise Miner database.     

An Equilibrium Model of Health Insurance Provision and Wage Determination

We investigate the effect of employer‐provided health insurance on job mobility rates and economic welfare using a search, matching, and bargaining framework. In our model, health insurance coverage decisions are made in a cooperative manner that recognizes the productivity effects of health insurance as well as its nonpecuniary value to the employee. The resulting equilibrium is one in which not all employment matches are covered by health insurance, wages at jobs providing health insurance are larger (in a stochastic sense) than those at jobs without health insurance, and workers at jobs with health insurance are less likely to leave those jobs, even after conditioning on the wage rate. We estimate the model using the 1996 panel of the Survey of Income and Program Participation, and find that the employer‐provided health insurance system does not lead to any serious inefficiencies in mobility decisions.     

An Insurance Model for Risk Management of Process Facilities

Most existing risk management models for process industries do not consider the effect of insurance coverage, which results in an overestimation of overall risk. A model is presented in this article to study the effect of insurance coverage of health, safety, environmental, and business risks. The effect of insurance recovery is modeled through the application of adjustment factors by considering the stochastic factors affecting insurance recovery. The insurance contract's conditions, deductibles, and policy limits are considered in developing the insurance recovery adjustment factors. Copula functions and Monte Carlo simulations are used to develop the distribution of the aggregate loss by considering the dependence among loss classes. A case study is used to demonstrate both the practical application of the proposed insurance model to improve management decisions, and the mitigating effect of insurance to minimize the residual risk.     

The Implications of Temporary Jobs on the Distribution of Wages in Italy: An Unconditional IVQTE Approach

Using Italian data, this paper investigates the wage implications of temporary jobs across the whole pay profile using unconditional quantile regression (UQR) models. Results clearly indicate that the wage penalty associated to temporary jobs is significantly larger at the bottom of wage profile and is almost absent for high‐wage jobs. This is in line with the sticky floors hypothesis, supporting the idea that the wage gap for temporary employees depends on their position in the wage distribution for low‐paid jobs. To recover a causal interpretation, I employ an instrumental variable (IV) strategy. I adopt the unconditional instrumental variable quantile treatment effects (IVQTE) estimator proposed by Frolich and Melly, which corrects for endogenous selection in temporary contracts. The IVQTE estimates yield similar results to standard UQR, even if the wage penalty is larger in size at the bottom of the wage distribution and disappears at the top quantiles. This evidence highlights that policies aimed at increasing flexibility may reinforce the two‐tier nature of the Italian labour market and the relative wage inequality.     

Quantile Selection Models With an Application to Understanding Changes in Wage Inequality

We propose a method to correct for sample selection in quantile regression models. Selection is modeled via the cumulative distribution function, or copula, of the percentile error in the outcome equation and the error in the participation decision. Copula parameters are estimated by minimizing a method‐of‐moments criterion. Given these parameter estimates, the percentile levels of the outcome are readjusted to correct for selection, and quantile parameters are estimated by minimizing a rotated “check” function. We apply the method to correct wage percentiles for selection into employment, using data for the UK for the period 1978–2000. We also extend the method to account for the presence of equilibrium effects when performing counterfactual exercises.     

Equilibria in Health Exchanges: Adverse Selection versus Reclassification Risk

This paper studies regulated health insurance markets known as exchanges, motivated by the increasingly important role they play in both public and private insurance provision. We develop a framework that combines data on health outcomes and insurance plan choices for a population of insured individuals with a model of a competitive insurance exchange to predict outcomes under different exchange designs. We apply this framework to examine the effects of regulations that govern insurers' ability to use health status information in pricing. We investigate the welfare implications of these regulations with an emphasis on two potential sources of inefficiency: (i) adverse selection and (ii) premium reclassification risk. We find substantial adverse selection leading to full unraveling of our simulated exchange, even when age can be priced. While the welfare cost of adverse selection is substantial when health status cannot be priced, that of reclassification risk is five times larger when insurers can price based on some health status information. We investigate several extensions including (i) contract design regulation, (ii) self‐insurance through saving and borrowing, and (iii) insurer risk adjustment transfers.     

Defined contribution health care: future direction or fantasy?

Will payers embrace defined contribution plans as an alternative to traditional health insurance or is this new approach a pipe dream? Are consumers truly ready to make informed decisions on purchasing their own health care? This article explores barriers to defined contribution health plans, including consumer reluctance to take ownership of buying insurance and a preference for the cost predictability of liberal coverage in employer-sponsored programs versus MSAs or higher co-payment arrangements. For the ultimate form of defined contribution health care to work, several tax and insurance barriers must be overcome. As a practical matter, the author argues that the current employer-sponsored approach is the most efficient system for large employers.