Giving (Money) Back To Parents: Racial/Ethnic and Immigrant–Native Variation in Monetary Exchanges During the Transition to Adulthood

Scholars consistently find that parents provide economic support to their young adult children through the transition to adulthood. However, scholars rarely examine whether young adults contribute monetary resources to their parents. To test this proposition, I use the National Longitudinal Study of Adolescent to Adult Health, and the case of “money for living expenses.” Overall, monetary independence is the modal category for all groups. However, when I compare across groups, I find that White young adults with native-born parentage are more likely to report monetary independence, African Americans are more likely to report monetary interdependence and Asian and Latino children of immigrants are more likely to report child-to-parent assistance compared to each other, with SES explaining most, but not all, of these differences. I argue that young adult offspring in non-White families are more likely to provide monetary support to their parents during the transition to adulthood. These transfers may deplete resources for non-White young adults and may exacerbate racial/ethnic inequality during the transition to adulthood.     

Estimation of the marginal expected shortfall under asymptotic independence

We study the asymptotic behavior of the marginal expected shortfall when the two random variables are asymptotic independent but positively associated, which is modeled by the so-called tail dependent coefficient. We construct an estimator of the marginal expected shortfall, which is shown to be asymptotically normal. The finite sample performance of the estimator is investigated in a small simulation study. The method is also applied to estimate the expected amount of rainfall at a weather station given that there is a once every 100 years rainfall at another weather station nearby.     

Bargaining and Reputation

The paper develops a reputation based theory of bargaining. The idea is to investigate and highlight the influence of bargaining ‘postures’ on bargaining outcomes. A complete information bargaining model a la Rubinstein is amended to accommodate ‘irrational types’ who are obstinate, and indeed for tractability assumed to be completely inflexible in their offers and demands. A strong ‘independence of procedures’ result is derived: after initial postures have been adopted, the bargaining outcome is independent of the fine details of the bargaining protocol so long as both players have the opportunity to make offers frequently. The latter analysis yields a unique continuous‐time limit with a war of attrition structure. In the continuous‐time game, equilibrium is unique, and entails delay, consequently inefficiency. The equilibrium outcome reflects the combined influence of the rates of time preference of the players and the ex ante probabilities of different irrational types. As the probability of irrationality goes to zero, delay and inefficiency disappear; furthermore, if there is a rich set of types for both agents, the limit equilibrium payoffs are inversely proportional to their rates of time preference.     

扶贫专项资金绩效审计优化探究

：我国脱贫攻坚战已近收官，扶贫专项资金落到实处并发挥最大效用，有赖于加强扶贫专项资金绩效审计。而目前扶贫资金绩效审计工作中，多存在重视程度较低、审计技术落后、审计力量薄弱和绩效评价体系不健全等问题。解决这些问题，就要加强扶贫资金绩效审计工作，健全相关法律政策体系，以信息化创新审计技术，培训审计人员复合性审计技能，建立健全绩效审计评价体系。     

Bayesian Network Structure Learning with Permutation Tests

In literature there are several studies on the performance of Bayesian network structure learning algorithms. The focus of these studies is almost always the heuristics the learning algorithms are based on, i.e., the maximization algorithms (in score-based algorithms) or the techniques for learning the dependencies of each variable (in constraint-based algorithms). In this article, we investigate how the use of permutation tests instead of parametric ones affects the performance of Bayesian network structure learning from discrete data. Shrinkage tests are also covered to provide a broad overview of the techniques developed in current literature.     

Approximating hidden Gaussian Markov random fields

Summary.  Gaussian Markov random-field (GMRF) models are frequently used in a wide variety of applications. In most cases parts of the GMRF are observed through mutually independent data; hence the full conditional of the GMRF, a hidden GMRF (HGMRF), is of interest. We are concerned with the case where the likelihood is non-Gaussian, leading to non-Gaussian HGMRF models. Several researchers have constructed block sampling Markov chain Monte Carlo schemes based on approximations of the HGMRF by a GMRF, using a second-order expansion of the log-density at or near the mode. This is possible as the GMRF approximation can be sampled exactly with a known normalizing constant. The Markov property of the GMRF approximation yields computational efficiency.The main contribution in the paper is to go beyond the GMRF approximation and to construct a class of non-Gaussian approximations which adapt automatically to the particular HGMRF that is under study. The accuracy can be tuned by intuitive parameters to nearly any precision. These non-Gaussian approximations share the same computational complexity as those which are based on GMRFs and can be sampled exactly with computable normalizing constants. We apply our approximations in spatial disease mapping and model-based geostatistical models with different likelihoods, obtain procedures for block updating and construct Metropolized independence samplers.     

Measurement Error Correction by Exploiting Gene–Environment Independence in Family‐Based Case–Control Studies

Abstract. Family‐based case–control designs are commonly used in epidemiological studies for evaluating the role of genetic susceptibility and environmental exposure to risk factors in the etiology of rare diseases. Within this framework, it is often reasonable to assume genetic susceptibility and environmental exposure being conditionally independent of each other within families in the source population. We focus on this setting to explore the situation of measurement error affecting the assessment of the environmental exposure. We correct for measurement error through a likelihood‐based method. We exploit a conditional likelihood approach to relate the probability of disease to the genetic and the environmental risk factors. We show that this approach provides less biased and more efficient results than that based on logistic regression. Regression calibration, instead, provides severely biased estimators of the parameters. The comparison of the correction methods is performed through simulation, under common measurement error structures.     

Approximation of power in multivariate analysis

We consider the calculation of power functions in classical multivariate analysis. In this context, power can be expressed in terms of tail probabilities of certain noncentral distributions. The necessary noncentral distribution theory was developed between the 1940s and 1970s by a number of authors. However, tractable methods for calculating the relevant probabilities have been lacking. In this paper we present simple yet extremely accurate saddlepoint approximations to power functions associated with the following classical test statistics: the likelihood ratio statistic for testing the general linear hypothesis in MANOVA; the likelihood ratio statistic for testing block independence; and Bartlett's modified likelihood ratio statistic for testing equality of covariance matrices.     

对我国法院体制现代化的思考

我国当前的法院体制改革是司法体制改革中非常重要和关键的一个环节,也是我国始于清末的近百余年法院体制现代化历程中最具有历史意义的一个阶段。以司法独立为目标,以构建科学合理的具体制度为内容的法院体制现代化经历了百余年曲折艰难的历程,在21世纪的今天仍将继续其前进的步伐,走向更完善更科学的未来。     

将审计风险理论引入内部审计实践

审计风险 ,是指会计报表存在重大错报或漏报 ,而注册会计师审计后发表不恰当审计意见的可能性。在审计界审计风险日益得到认识和重视 ,内部审计人员有责任探究审计风险理论 ,充分认识审计风险的概念、特征及其产生的原因 ,探索审计风险的防范措施 ,并将其运用到内部审计实践当中 ,建立控制审计风险体系 ,从而达到规避审计风险 ,提高审计工作质量 ,巩固内审部门地位的目的