人口老龄化对积极财政政策有效性的影响——兼析延迟退休的宏观经济效应

人口老龄化不但影响到宏观经济的稳定,也会对财政政策的有效性产生影响。将人口老龄化引入动态随机一般均衡模型,分析人口老龄化对4种积极财政政策工具(扩大政府支出、提高劳动所得税起征点、提高劳动所得税累进性和增加公共投资)有效性产生的影响以及延迟退休的动态宏观经济效应,结果表明:人口老龄化扩大了政府支出和政府公共投资对消费产生的挤出效应,降低了4种积极财政政策工具对产出、消费、就业的促进效应,也强化了其对通胀和工资膨胀的提升效应;延迟退休未对私人消费产生挤出效应,有利于就业、产出和消费增长,并有助于稳定通胀和工资膨胀;与财政政策工具相比,延迟退休引致的整体社会福利损失较小。人口老龄化降低了积极财政政策的有效性,基于稳定宏观经济和降低社会福利损失的考虑,延迟退休是一个较优的政策选择。     

A mixed-effects least square support vector regression model for three-level count data

Hierarchical study design often occurs in many areas such as epidemiology, psychology, sociology, public health, engineering, and agriculture. This imposes correlation in data structure that needs to be account for in modelling process. In this study, a three-level mixed-effects least squares support vector regression (MLS-SVR) model is proposed to extend the standard least squares support vector regression (LS-SVR) model for handling cluster correlated data. The MLS-SVR model incorporates multiple random effects which allow handling unequal number of observations for each case at non-fixed time points (a very unbalanced situation) and correlation between subjects simultaneously. The methodology consists of a regression modelling step that is performed straightforwardly by solving a linear system. The proposed model is illustrated through numerical studies on simulated data sets and a real data example on human Brucellosis frequency. The generalization performance of the proposed MLS-SVR is evaluated by comparing to ordinary LS-SVR and some other parametric models.     

The cluster correlation-network support vector machine for high-dimensional binary classification

ABSTRACT

Identifying homogeneous subsets of predictors in classification can be challenging in the presence of high-dimensional data with highly correlated variables. We propose a new method called cluster correlation-network support vector machine (CCNSVM) that simultaneously estimates clusters of predictors that are relevant for classification and coefficients of penalized SVM. The new CCN penalty is a function of the well-known Topological Overlap Matrix whose entries measure the strength of connectivity between predictors. CCNSVM implements an efficient algorithm that alternates between searching for predictors’ clusters and optimizing a penalized SVM loss function using Majorization–Minimization tricks and a coordinate descent algorithm. This combining of clustering and sparsity into a single procedure provides additional insights into the power of exploring dimension reduction structure in high-dimensional binary classification. Simulation studies are considered to compare the performance of our procedure to its competitors. A practical application of CCNSVM on DNA methylation data illustrates its good behaviour.     

Optimal exact design of double acceptance sampling plans by attributes

The design of double acceptance sampling (AS) plans for attributes based on the operating characteristic curve paradigm is usually addressed by enumeration algorithms. These AS plans may be non optimal regarding the sample size to inspect as they were obtained without the requirement that the constraints at the OC curve controlled points are not violated for minimum Average Sample Number (ASN) scenarios. An approach based on mathematical programming is proposed to systematically design double AS plans for attributes, where the characteristics controlled are modelled by binomial or Poisson distributions. Specifically, Mixed Integer Nonlinear Programming (MINLP) formulations are developed and combined with an enumeration algorithm that allows finding ASN minimax optimal plans. A theoretical result is developed with the purpose of assuring the global optimum design is reached by iteration where a convenient solver is used to find local optima. To validate the algorithm, we compare our results with those of tables commonly used for practical purposes, consider different rates of risk, and setups commonly used in Lot Quality Assurance Plans (LQAS) for health monitoring programmes. Finally, we compare AS plans determined for processes described by binomial and Poisson distributions.     

Specification testing in nonparametric AR‐ARCH models

In this paper, an autoregressive time series model with conditional heteroscedasticity is considered, where both conditional mean and conditional variance function are modeled nonparametrically. Tests for the model assumption of independence of innovations from past time series values are suggested. Tests based on weighted L²‐distances of empirical characteristic functions are considered as well as a Cramér–von Mises‐type test. The asymptotic distributions under the null hypothesis of independence are derived, and the consistency against fixed alternatives is shown. A smooth autoregressive residual bootstrap procedure is suggested, and its performance is shown in a simulation study.     

Estimation bias and bias correction in reduced rank autoregressions

This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root-finding algorithm implemented using stochastic approximation methods. Both algorithms are shown to be improvements over the MLE, measured in terms of mean square error and mean absolute deviation. An illustration to US macroeconomic time series is given.     

Choosing between the BP and BN sequential strategies

Cost and burden of diagnostic testing may be reduced if fewer tests can be applied. Sequential testing involves selecting a sequence of tests, but only administering subsequent tests dependent on results of previous tests. This research provides guidance to choosing between single tests or the believe the positive (BP) and believe the negative (BN) sequential testing strategies, using accuracy (as measured by the Youden Index) as the primary determinant. Approximately 75% of the parameter combinations examined resulted in either BP or BN being recommended based on a higher accuracy at the optimal point. In about half of the scenarios BP was preferred, and the other half, BN, with the choice often a function of the value of the ratio of standard deviations of those without and with disease (b). Large values of b for the first test of the sequence tended to be associated with preference for BN as opposed to BP, while small values of b appear to favor BP. When there was no preference between sequences and/or single tests based on the Youden Index, cost of the sequence was considered. In this case, disease prevalence plays a large role in the selection of strategies, with lower values favoring BN and sometimes higher values favoring BP. The cost threshold for the sequential strategy to be preferred over a single, more accurate test, was often quite high. It appears that while sequential strategies most often increase diagnostic accuracy over a single test, sequential strategies are not always preferred.     

中国特色社会主义理论体系的时代性及其实践主题

中国特色社会主义理论体系具有鲜明的时代特色,时代性是中国特色社会主义理论体系的根本属性：宏观战略上的改革开放、综合建设上的快速发展以及指导思想上的与时俱进,都凸现着中国特色社会主义理论体系的时代性特征;科学发展与社会和谐构成了中国特色社会主义理论体系的实践主题。     

Countertransference and positive growth in social workers

A number of factors contribute to social workers' states of mind including exposure to intense emotionality and disturbance, job satisfaction, burnout, secondary stress, and the perceived value attached to social work. Together these can lead to placing social workers at risk of vicarious traumatisation. While vicarious traumatisation can lead to posttraumatic stress, it may be important to explore whether posttraumatic growth is a possibility. The aim was to provide evidence for growth in social workers and to test for its association with factors that are known to contribute to the state of mind of the social worker, and thus affect countertransference reactions. Sixty-two social workers completed self-report measures showing those who felt more valued in their professional role scored lower on burnout, higher on job satisfaction, and positive growth. The role of these variables as factors influencing countertransference reactions is discussed.     

Models for Dependent Extremes Using Stable Mixtures

Abstract.  This paper unifies and extends results on a class of multivariate extreme value (EV) models studied by Hougaard, Crowder and Tawn. In these models, both unconditional and conditional distributions are themselves EV distributions, and all lower-dimensional marginals and maxima belong to the class. One interpretation of the models is as size mixtures of EV distributions, where the mixing is by positive stable distributions. A second interpretation is as exponential-stable location mixtures (for Gumbel) or as power-stable scale mixtures (for non-Gumbel EV distributions). A third interpretation is through a peaks over thresholds model with a positive stable intensity. The mixing variables are used as a modelling tool and for better understanding and model checking. We study EV analogues of components of variance models, and new time series, spatial and continuous parameter models for extreme values. The results are applied to data from a pitting corrosion investigation.