Conditional VAR and Expected Shortfall: A New Functional Approach

We estimate two well-known risk measures, the value-at-risk (VAR) and the expected shortfall, conditionally to a functional variable (i.e., a random variable valued in some semi(pseudo)-metric space). We use nonparametric kernel estimation for constructing estimators of these quantities, under general dependence conditions. Theoretical properties are stated whereas practical aspects are illustrated on simulated data: nonlinear functional and GARCH(1,1) models. Some ideas on bandwidth selection using bootstrap are introduced. Finally, an empirical example is given through data of the S&P 500 time series.     

Robust Inference for Near-Unit Root Processes with Time-Varying Error Variances

The autoregressive Cauchy estimator uses the sign of the first lag as instrumental variable (IV); under independent and identically distributed (i.i.d.) errors, the resulting IV t-type statistic is known to have a standard normal limiting distribution in the unit root case. With unconditional heteroskedasticity, the ordinary least squares (OLS) t statistic is affected in the unit root case; but the paper shows that, by using some nonlinear transformation behaving asymptotically like the sign as instrument, limiting normality of the IV t-type statistic is maintained when the series to be tested has no deterministic trends. Neither estimation of the so-called variance profile nor bootstrap procedures are required to this end. The Cauchy unit root test has power in the same 1/T neighborhoods as the usual unit root tests, also for a wide range of magnitudes for the initial value. It is furthermore shown to be competitive with other, bootstrap-based, robust tests. When the series exhibit a linear trend, however, the null distribution of the Cauchy test for a unit root becomes nonstandard, reminiscent of the Dickey-Fuller distribution. In this case, inference robust to nonstationary volatility is obtained via the wild bootstrap.     

Stochastic analysis of covariance when the error distribution is long-tailed symmetric

In this study, we consider stochastic one-way analysis of covariance model when the distribution of the error terms is long-tailed symmetric. Estimators of the unknown model parameters are obtained by using the maximum likelihood (ML) methodology. Iteratively reweighting algorithm is used to compute the ML estimates of the parameters. We also propose new test statistic based on ML estimators for testing the linear contrasts of the treatment effects. In the simulation study, we compare the efficiencies of the traditional least-squares (LS) estimators of the model parameters with the corresponding ML estimators. We also compare the power of the test statistics based on LS and ML estimators, respectively. A real-life example is given at the end of the study.     

Bayesian variable selection approach to a Bernstein polynomial regression model with stochastic constraints

This paper provides a Bayesian estimation procedure for monotone regression models incorporating the monotone trend constraint subject to uncertainty. For monotone regression modeling with stochastic restrictions, we propose a Bayesian Bernstein polynomial regression model using two-stage hierarchical prior distributions based on a family of rectangle-screened multivariate Gaussian distributions extended from the work of Gurtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]]. This approach reflects the uncertainty about the prior constraint, and thus proposes a regression model subject to monotone restriction with uncertainty. Based on the proposed model, we derive the posterior distributions for unknown parameters and present numerical schemes to generate posterior samples. We show the empirical performance of the proposed model based on synthetic data and real data applications and compare the performance to the Bernstein polynomial regression model of Curtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]] for the shape restriction with certainty. We illustrate the effectiveness of our proposed method that incorporates the uncertainty of the monotone trend and automatically adapts the regression function to the monotonicity, through empirical analysis with synthetic data and real data applications.     

中国城市化进程效率估算及影响因素分析

本文基于2008—2012年中国285个地级市的数据,运用双边随机前沿模型测算了中国城市化进程的前沿水平以及实际水平相对前沿水平的偏离程度。结果发现,下偏效应大于上偏效应,即中国城市化进程整体表现为滞后,并且发现投资作用和政府作用在影响城市化进程中的表现为负向,另外城市化进程也表现出地区间的不平衡。最后根据本文的发现,提出相应的建议。     

“镇改市”过程中的制约因素与改革进路

“镇改市”是我国基层行政体制改革的试验,也是在新型城镇化过程中对基层行政区划改革做出的有益尝试。以构建大中小城市和小城镇协调发展的城镇化空间格局为目标指向,通过激活中小城市的发展“命脉”,改变单纯依靠经济推动乡镇发展的路径依赖,实现“农民城”向新型城镇的跨越,是中国经济发展的新探索。然而,随着改革的推进,缺位的行政规划设计、有限的乡镇管理能力、被异化的政绩观念、社会组织发育不成熟以及市民文化普及不到位等,对“镇改市”的推进造成一定程度的制约,成为“镇改市”过程中难以回避的现实问题。问题的化解离不开政策的引导、技术的支持和法治的保障,这三个维度相互联系、缺一不可,共同为“镇改市”改革的顺利实施“保驾护航”。     

大型科研项目的风险管理流程与风险决策

针对大型科研项目风险管理的特点,设计了其风险管理流程;根据风险决策原理,结合实例探讨了灰局势决策法在大型科研项目中的应用,所获得的决策结果是进行风险处理的基础和重要依据,对风险管理实践有一定的指导意义。     

在内外因的交互作用中把握德育过程

德育是一个综合化、系统化的内外因交互作用的过程,它既包括个人的内化过程,也包括诸如家庭、学校、社会的外塑过程。只有认真把握内化和外塑这两个过程的特点和规律,不断创新德育工作方法,才能使德育工作收到实效。本文着重论述了德育过程中个人"内化"过程的主动性、自主性、独立性、体验性,以及作为德育"外塑"过程的家庭德育过程、学校德育过程、社会德育过程各自的地位、特点、存在的问题以及解决问题的方法,以期对做好德育工作起到积极作用。