A Discrete Variable Chain Graph for Applicants for Credit

A bank offering unsecured personal loans may be interested in several related outcome variables, including defaulting on the repayments, early repayment or failing to take up an offered loan. Current predictive models used by banks typically consider such variables individually. However, the fact that they are related to each other, and to many interrelated potential predictor variables, suggests that graphical models may provide an attractive alternative solution. We developed such a model for a data set of 15 variables measured on a set of 14 000 applications for unsecured personal loans. The resulting global model of behaviour enabled us to identify several previously unsuspected relationships of considerable interest to the bank. For example, we discovered important but obscure relationships between taking out insurance, prior delinquency with a credit card and delinquency with the loan.     

Statistical analysis of rock-burst events in underground mines and excavations to present reasonable data-driven predictors

Rock bursts are sudden and violent failures of surrounding rockmasses in underground mines and excavations. In this paper, a database consisting of 188 case histories was collected. Each case history contains some of the predictor variables ‘overburden thickness, maximum tangential stress, uniaxial compressive strength of rock, tensile strength of rock, stress ratio, brittleness ratio and elastic energy index’ and one of the four defined classes for the dependent variable ‘rock burst intensity’. A strategy, including ‘outlier detection and substitution, normality evaluation, deduction of distribution functions, estimation of mean and mean variation ranges, evaluation of mean-equality and distribution function-equality hypotheses, correlation analysis and factor analysis for in-review variables’, was implemented. The strategy led to conclude that some predictor variables with available case histories have no contributions for rock burst prediction. These inferences were in accordance with the results of regression techniques for qualitative dependent variables. Besides, many predictor variable arrangements were incompatible with factor analysis. In the case of compatible arrangements, the variation of the predictor variables cannot be considerably reflected. Application of nonlinear principal component analysis using auto-associative neural networks did not also lead to representative components. Therefore, the significant predictor variables can only be used to design new classifiers.     

Categorical multiblock linear discriminant analysis

Techniques of credit scoring have been developed these last years in order to reduce the risk taken by banks and financial institutions in the loans that they are granting. Credit Scoring is a classification problem of individuals in one of the two following groups: defaulting borrowers or non-defaulting borrowers. The aim of this paper is to propose a new method of discrimination when the dependent variable is categorical and when a large number of categorical explanatory variables are retained. This method, Categorical Multiblock Linear Discriminant Analysis, computes components which take into account both relationships between explanatory categorical variables and canonical correlation between each explanatory categorical variable and the dependent variable. A comparison with three other techniques and an application on credit scoring data are provided.     

A Family of Near-Exact Distributions Based on Truncations of the Exact Distribution for the Generalized Wilks Lambda Statistic

For the case where at least two sets have an odd number of variables we do not have the exact distribution of the generalized Wilks Lambda statistic in a manageable form, adequate for manipulation. In this article, we develop a family of very accurate near-exact distributions for this statistic for the case where two or three sets have an odd number of variables. We first express the exact characteristic function of the logarithm of the statistic in the form of the characteristic function of an infinite mixture of Generalized Integer Gamma distributions. Then, based on truncations of this exact characteristic function, we obtain a family of near-exact distributions, which, by construction, match the first two exact moments. These near-exact distributions display an asymptotic behaviour for increasing number of variables involved. The corresponding cumulative distribution functions are obtained in a concise and manageable form, relatively easy to implement computationally, allowing for the computation of virtually exact quantiles. We undertake a comparative study for small sample sizes, using two proximity measures based on the Berry-Esseen bounds, to assess the performance of the near-exact distributions for different numbers of sets of variables and different numbers of variables in each set.     

Reducing errors-in-variables bias in linear regression using compact genetic algorithms

A new technique is devised to mitigate the errors-in-variables bias in linear regression. The procedure mimics a 2-stage least squares procedure where an auxiliary regression which generates a better behaved predictor variable is derived. The generated variable is then used as a substitute for the error-prone variable in the first-stage model. The performance of the algorithm is tested by simulation and regression analyses. Simulations suggest the algorithm efficiently captures the additive error term used to contaminate the artificial variables. Regressions provide further credit to the simulations as they clearly show that the compact genetic algorithm-based estimate of the true but unobserved regressor yields considerably better results. These conclusions are robust across different sample sizes and different variance structures imposed on both the measurement error and regression disturbances.     

Tolerance regions for random vectors and best linear predictors

In the case that vectors X and Y have a joint multivariate normal distribution, tolerance regions are found for the best linear predictor of Y using X if samples are used to estimate the regression coeffierante. Tolerance regions are also found for Y. In addition, simultaneous tolerance intervals for all linear functions of Y or of the best linear predictor of Y using X are found.     

经济地位对信用卡逾期的影响

我国信用卡业务的迅猛发展助推了消费经济的快速发展，但信用卡的逾期行为不容忽视。收入代表了一个人的经济地位，是信用卡按时还款的保障。本文基于某商业银行信用卡客户的逾期数据，以持卡人的经济地位为视角，分析了经济地位对信用卡逾期行为的影响。研究结果表明，我国商业银行信用卡持卡人的逾期行为具有显著的经济特征，收入对信用卡逾期的影响呈“U”型的非线性特征，即收入较低和收入较高的持卡人逾期的可能性较高，收入处于中间的持卡人逾期的可能性较低。进一步的研究发现，中年群体、工作单位稳定者、有房者会降低经济地位对信用卡逾期行为的非线性影响，而账龄较长的持卡人提升了这种影响。本文的研究为全社会建立良好的信用卡用卡环境，商业银行高效处理信用卡逾期，改进和完善商业银行信用卡风险管理提供了关键证据。     

Bootstrapping mean‐squared errors of robust small‐area estimators: Application to the method‐of‐payments surveys data

This paper proposes a new bootstrap procedure for mean‐squared errors of robust small‐area estimators. We formally prove the asymptotic validity of the proposed bootstrap method and examine its finite‐sample performance through Monte Carlo simulations. The results show that our procedure performs well and competes with existing ones. We also provide an application to the estimation of the total volume and value of cash, debit card, and credit card transactions in Canada as well as in its provinces and subgroups of households. In particular, we found that there is a significant average annual decline rate of 3.1% in the volume of cash transactions and that this decline is relatively higher among high‐income households living in heavily populated provinces. Our bootstrap estimator also provides indicators of quality useful in selecting the best small‐area predictor among several alternatives in practice.     

“做实做小”个人账户对个人养老金水平的影响

 本文引入养老保险金记账利率和工资增长率，测算了在不同初始缴费工资和缴费年限条件下实施“做实做小”个人账户政策对参保人养老金水平的影响。通过分析得出：1.政策调整后，养老金给付结构较调整前更能体现缴费积累与养老金水平的内在经济联系； 2.政策调整后个人养老金水平普遍不如政策调整前的水平，政策调整后养老金水平高于政策调整前均出现在缴费年限较长以及初始缴费工资较高的情况下；3.政策调整对不同的参保人影响不同。政策调整加大了低初始缴费工资人群的生活压力；拉大了男、女职工退休养老金的差距，不利于当前社会男女平等和贫富分化问题的解决。     

Modelling interval data with Normal and Skew-Normal distributions

A parametric modelling for interval data is proposed, assuming a multivariate Normal or Skew-Normal distribution for the midpoints and log-ranges of the interval variables. The intrinsic nature of the interval variables leads to special structures of the variance–covariance matrix, which is represented by five different possible configurations. Maximum likelihood estimation for both models under all considered configurations is studied. The proposed modelling is then considered in the context of analysis of variance and multivariate analysis of variance testing. To access the behaviour of the proposed methodology, a simulation study is performed. The results show that, for medium or large sample sizes, tests have good power and their true significance level approaches nominal levels when the constraints assumed for the model are respected; however, for small samples, sizes close to nominal levels cannot be guaranteed. Applications to Chinese meteorological data in three different regions and to credit card usage variables for different card designations, illustrate the proposed methodology.     

Do Central Bank Liquidity Facilities Affect Interbank Lending Rates?

In response to the global financial crisis that started in August 2007, central banks provided extraordinary amounts of liquidity to the financial system. To investigate the effect of central bank liquidity facilities on term interbank lending rates near the start of the crisis, we estimate a six-factor arbitrage-free model of U.S. Treasury yields, financial corporate bond yields, and term interbank rates. This model can account for fluctuations in the term structure of credit and liquidity spreads observed in the data. A significant shift in model estimates after the announcement of the liquidity facilities suggests that these central bank actions did help lower the liquidity premium in term interbank rates.     

中国省域信贷风险判别分析

现阶段，商业银行信贷仍是我国社会资金配置的主要方式。出于盈利和风险考虑，商业银行信贷行为天然具有顺周期特征。为实现稳增长目标，政府更倾向于逆周期调节。受到地方财政收支状况影响，省级地方政府会采取不同方式、不同程度地干预省域资金配置。十九大报告明确指出，要健全货币政策和宏观审慎政策双支柱调控框架。因此，省域信贷风险判别是一个动态过程，需在经济周期与宏观审慎政策框架下整体考虑。在此背景下，本文基于新古典经济学分析框架，建立了2008年以来省域信贷风险识别模型，研究发现，第一，地方财政支出收入比与不良贷款率存在正向影响关系，资本回报率与不良贷款率存在负向影响关系，且地方财政支出收入比对不良贷款率的影响程度更大；第二，依据分类准则，属于信贷高风险的省域分别是：河南，海南，重庆，四川，贵州，云南，陕西，甘肃，青海，宁夏，新疆，西藏；第三，在地方财政支出收入比、资本回报率的显著作用影响下，我国各省域不良贷款率呈现U型变化，不良贷款率阈值为1.49%，即当不良贷款率大于1.49%时，省域贷款风险较高；第四，当我国资本回报率处于企稳阶段，不良贷款率处于低于阈值的谷底阶段，且省域间风险差异性较小。当我国资本回报率处于下行阶段时，不良贷款率上升至阈值线以上，且省域间风险差异性较大。     

A unified approach to estimation of nonlinear mixed effects and Berkson measurement error models

Mixed effects models and Berkson measurement error models are widely used. They share features which the author uses to develop a unified estimation framework. He deals with models in which the random effects (or measurement errors) have a general parametric distribution, whereas the random regression coefficients (or unobserved predictor variables) and error terms have nonparametric distributions. He proposes a second-order least squares estimator and a simulation-based estimator based on the first two moments of the conditional response variable given the observed covariates. He shows that both estimators are consistent and asymptotically normally distributed under fairly general conditions. The author also reports Monte Carlo simulation studies showing that the proposed estimators perform satisfactorily for relatively small sample sizes. Compared to the likelihood approach, the proposed methods are computationally feasible and do not rely on the normality assumption for random effects or other variables in the model.     

商业银行信用风险分析的主要技术

 加入WTO以后，国内商业银行面临着严峻的考验。大量的信贷资产质量低下，贷款不良率仍然很高。从1999年开始，国家对四大国有商业银行不良资产实行了剥离政策。但即便如此，中国建设银行、中国商业银行和中国银行不良贷款率仍比人民银行规定的15%的控制水平要高。剥离不良资产后，信贷资产质量仍然较差。这说明政策性的剥离不良资产措施只能解决不良资产的存在问题。要从本质上改善信贷资产的质量，商业银行就必须从自身的信贷风险管理的角度来采取强有力的措施。本文综述了国内外商业银行信用风险管理的技术，旨在为商业银行信用风险管理的研究提供借鉴。     

Likelihood estimation for a longitudinal negative binomial regression model with missing outcomes

Joint damage in psoriatic arthritis can be measured by clinical and radiological methods, the former being done more frequently during longitudinal follow-up of patients. Motivated by the need to compare findings based on the different methods with different observation patterns, we consider longitudinal data where the outcome variable is a cumulative total of counts that can be unobserved when other, informative, explanatory variables are recorded. We demonstrate how to calculate the likelihood for such data when it is assumed that the increment in the cumulative total follows a discrete distribution with a location parameter that depends on a linear function of explanatory variables. An approach to the incorporation of informative observation is suggested. We present analyses based on an observational database from a psoriatic arthritis clinic. Although the use of the new statistical methodology has relatively little effect in this example, simulation studies indicate that the method can provide substantial improvements in bias and coverage in some situations where there is an important time varying explanatory variable.     

养老保险个人账户参数设定错误与纠正

我国基本养老保险采取统账结合的制度模式。其中，个人账户采用记账方式建立了参保缴费与养老待遇之间的直接联系。理论上，公平和长期可持续的个人账户模式在记账利率、计发系数、余额继承等方面应遵循精算原则，但我国的个人账户参数设定存在不少违背精算公平和精算平衡原则的错误。本文结合国际经验，在剖析我国养老保险个人账户参数设定错误的基础上，基于精算平衡原理，探讨参数设定方法，检验纠正方案对制度精算公平和精算平衡性的效果，最后提出参数改革的具体建议。主要结论是：个人账户的记账利率应该是制度的内含回报率；名义账户的记账利率不是银行存款利率，而应随缴费工资增长率、人口预期寿命的变动等定期调整；养老金计发系数应基于动态生命表、养老金调整指数和个人账户内含回报率的变动而调整。由此确定的参数纠正方案将有效改善制度的精算公平与精算平衡性。因而建议我国基本养老保险个人账户应尽早明确现收现付的筹资模式和账户余额的权益归属，采用动态的记账利率和计发月数，引入自动平衡机制，实现制度的长期精算平衡。     

The Way We Pay With Money

We assess the sample distribution of payments in the Netherlands using survey data for 1984–1986. The payment pattern fits a lognormal distribution with peaks caused by the price tags of the purchased items. The results are used to simulate the use of coins and notes, which indicates a rather efficient denominational mix but a high degree of hoarding for the large bank notes.     

A mixture latent variable model for modeling mixed data in heterogeneous populations and its applications

Latent variable models are widely used for jointly modeling of mixed data including nominal, ordinal, count and continuous data. In this paper, we consider a latent variable model for jointly modeling relationships between mixed binary, count and continuous variables with some observed covariates. We assume that, given a latent variable, mixed variables of interest are independent and count and continuous variables have Poisson distribution and normal distribution, respectively. As such data may be extracted from different subpopulations, consideration of an unobserved heterogeneity has to be taken into account. A mixture distribution is considered (for the distribution of the latent variable) which accounts the heterogeneity. The generalized EM algorithm which uses the Newton–Raphson algorithm inside the EM algorithm is used to compute the maximum likelihood estimates of parameters. The standard errors of the maximum likelihood estimates are computed by using the supplemented EM algorithm. Analysis of the primary biliary cirrhosis data is presented as an application of the proposed model.     

企业财务风险预测模型实证比较分析

企业财务风险一直是风险管理理论和实务界关心的热点话题。运用判别分析和计量经济方法对重庆市某商业银行的461个样本企业2002-2005年的违约特征进行实证检验和预测。结果发现最重要的决定变量是资产负责率、酸性试验比率、资产净利率等7个财务比率以及企业所处的产业部门,考虑了异方差性的probit模型有更好的预测能力。     

利率扭曲、市场分割与深化利率市场化改革

虽然存贷款利率管制已经取消,但在改革过渡期内,目前我国信贷市场中仍然存在一定程度的利率扭曲与市场分割：同时存在商业银行和类银行机构,商业银行利率较低,主要为传统企业等低风险企业贷款,而非传统企业融资更多依赖于利率较高的类银行机构。本文从利率扭曲与市场分割问题入手,构建包含异质性金融部门和企业部门的动态随机一般均衡（DSGE）模型,研究深化利率市场化改革对经济金融的具体效应。结果显示,改革将促使利率回归均衡,消除监管套利,维护金融稳定;同时,优化金融资源配置,降低企业部门杠杆率,推动经济高质量发展。但改革也将对不同类型的金融、企业部门形成差异化影响,在助推产业结构优化升级的同时,驱动商业银行和传统企业加快经营转型。此外,改革后经济体在面对外部冲击时更加稳健。进一步,对比不同改革进程下的政策效果发现,若进程过快,可能增加短期经济金融波动风险。因此,改革应遵循循序渐进的推进原则。