我国市场债券收益的可预测性及其经济价值研究

债券收益的可预测性及其经济价值一直是颇具争议的热点问题.本文首先利用回归模型检验了我国债券收益的可预测性,并分析了债券收益的非马尔科夫性和随机波动特征.在此基础上,在广义随机波动HJM框架下提出了非马尔科夫DTSM模型的构建方法,并分析了非马尔科夫性和随机波动性对于债券超额收益的可预测性及其经济价值实现的作用.最后,考察了我国市场上债券收益可预测性的来源.结果表明,我国债券收益可预测性具有很强的统计显著性,且可以转化为显著的经济收益.在此过程中,非马尔科夫性、随机波动性具有十分关键的作用.经济环境驱动的时变性风险溢价是我国债券收益可预测性的主要来源,而非涵盖随机波动因子也显著含有债券收益的预测信息.     

The characterization of Monte Carlo errors for the quantification of the value of forensic evidence

Recent developments in forensic science have lead to a proliferation of methods for quantifying the probative value of evidence by constructing a Bayes Factor that allows a decision-maker to select between the prosecution and defense models. Unfortunately, the analytical form of a Bayes Factor is often computationally intractable. A typical approach in statistics uses Monte Carlo integration to numerically approximate the marginal likelihoods composing the Bayes Factor. This article focuses on developing a generally applicable method for characterizing the numerical error associated with Monte Carlo integration techniques used in constructing the Bayes Factor. The derivation of an asymptotic Monte Carlo standard error (MCSE) for the Bayes Factor will be presented and its applicability to quantifying the value of evidence will be explored using a simulation-based example involving a benchmark data set. The simulation will also explore the effect of prior choice on the Bayes Factor approximations and corresponding MCSEs.     

Objective Bayesian hypothesis testing in regression models with first-order autoregressive residuals

This article considers the objective Bayesian testing in the normal regression models with first-order autoregressive residuals. We propose some solutions based on a Bayesian model selection procedure to this problem where no subjective input is considered. We construct the proper priors for testing the autocorrelation coefficient based on measures of divergence between competing models, which is called the divergence-based (DB) priors and then propose the objective Bayesian decision-theoretic rule, which is called the Bayesian reference criterion (BRC). Finally, we derive the intrinsic test statistic for testing the autocorrelation coefficient. The behavior of the Bayes factor-based DB priors is examined by comparing with the BRC in a simulation study and an example.     

Bayesian analysis for confirmatory factor model with finite-dimensional Dirichlet prior mixing

Confirmatory factor analysis (CFA) model is a useful multivariate statistical tool for interpreting relationships between latent variables and manifest variables. Often statistical results based on a single CFA are seriously distorted when data set takes on heterogeneity. To address the heterogeneity resulting from the multivariate responses, we propose a Bayesian semiparametric modeling for CFA. The approach relies on using a prior over the space of mixing distributions with finite components. Blocked Gibbs sampler is implemented to cope with the posterior analysis. Results obtained from a simulation study and a real data set are presented to illustrate the methodology.     

Paternal Risk Factors for Child Maltreatment and Father's Participation in a Primary Prevention Program in Germany

ABSTRACT

Across the globe, the research into the influence of paternal risk and protective factors on the family, as well as on the involvement of fathers in family-related interventions, is lacking. This study utilized 506 families and examined the characteristics of fathers in psychosocially stressed families and associations between paternal risk factors (PRFs: mental health disorder, physical health disorder, young paternity, unemployment, absence of father) and family risk factors (FRFs: problematic financial situation, problematic housing situation, social isolation) for child maltreatment. The identification of PRFs and FRFs was carried out using information from consultations with social education workers and from a risk inventory completed by the mothers. The data analysis revealed an increased incidence of economic problems found in families with fathers having mental health issues, fathers under the age of 22, and unemployed fathers or single mothers. Other factors such as social isolation occurred more often in families with a single mother. The present study underlines the importance of a father's involvement for young families. Future research and program recommendations should always systematically include the father's role in family-related interventions.     

Mapping IS failure factors on PRINCE2® stages: an application of Interpretive Ranking Process (IRP)

The social, political and cultural issues faced by organisations and their senior management team in the delivery and adoption of strategic projects, is highly complex and problematic. Despite a mature body of literature, increasing levels of practitioner certification, application of standards and numerous government initiatives, improvements in success have been minimal. In this study, we analyse the key underlying factors surrounding the failure of Information Systems (IS) projects and explore the merits of articulating a narrative that focuses on senior management embracing practical pessimism. Specifically, we develop a hypothesis supported by empirical study that leverages expert’s views on the dominance and interrelationships between failure factors within PRINCE2® project stages using an Interpretive Ranking Process. Our findings establish how the concept of dominance between individual failure factors can necessitate senior management to make key informed and timely decisions that could potentially influence project outcomes based on an empirical derived, interpretive predictive framework.     

Mixtures of general location model with factor analyzer covariance structure for clustering mixed type data

Cluster analysis is one of the most widely used method in statistical analyses, in which homogeneous subgroups are identified in a heterogeneous population. Due to the existence of the continuous and discrete mixed data in many applications, so far, some ordinary clustering methods such as, hierarchical methods, k-means and model-based methods have been extended for analysis of mixed data. However, in the available model-based clustering methods, by increasing the number of continuous variables, the number of parameters increases and identifying as well as fitting an appropriate model may be difficult. In this paper, to reduce the number of the parameters, for the model-based clustering mixed data of continuous (normal) and nominal data, a set of parsimonious models is introduced. Models in this set are extended, using the general location model approach, for modeling distribution of mixed variables and applying factor analyzer structure for covariance matrices. The ECM algorithm is used for estimating the parameters of these models. In order to show the performance of the proposed models for clustering, results from some simulation studies and analyzing two real data sets are presented.     

行政立法的正当性初探

行政立法是社会发展的产物,它的产生和扩张有其经济、政治、科技等外在原因以及法制发展需要的内在原因。我们无法否定或取消行政立法的存在,但是行政立法确实存在种种弊端,这些弊端是由其自身的性质和特点决定的。我们必须加强对行政立法的监督控制,包括规则控制、程序控制、立法监督和司法监督,促进行政立法权的正当行使。     

西安高新区战略性新兴产业创新能力评价研究

战略性新兴产业具有战略性、成长性和创新性,科技创新是其发展的灵魂,研究其创新能力具有重要意义。综合运用因子分析和聚类分析对西安高新区七大战略性新兴产业24个细分行业创新能力进行评价,结果显示创新能力主要体现为创新转化能力、创新保障能力和创新实现能力。根据评价结果将产业划分为领先型、优势型和潜力型产业,在此基础上提出相关政策建议。