人力资源管理与企业战略、绩效关系研究的演进——基于企业环境的变化

高度动态化的环境作为当前企业面临的最大问题,导致不可预测性的增强。这将直接影响战略的制定和执行。因此,这一环境下的企业如何保持战略实施有效性和竞争优势可持续性成为企业发展道路上的关键。而在战略与绩效的关系中,人力资源作为企业关键的战略性资源是企业运作的基础,其管理起了非常重要的作用。本文通过对已有文献的详细梳理,概括出两类研究类型:战略和人力资源的匹配;人力资源的配置管理与实现绩效的关系。柔性化思想的提出,使人力资源研究有了新的方向。在环境变动这一前提下,人力资源柔性化的管理区别于传统意义上的管理。本文以动态环境的背景,围绕人力资源、战略、绩效三者的关系提炼出整合的演进框架,以更好地指导未来人力资源相关的研究。     

Patience moderates the class cleavage in demand for redistribution

Previous studies on class voting have yielded mixed results linking income and demand for redistribution. Why do some poor people oppose redistribution, while some rich people support it? This article argues that an individual's level of patience, an important personal characteristic that influences how people calculate immediate and distinct outcomes, may moderate the effect of class on redistributive preference. In a one-shot game, redistribution between the rich and the poor is zero sum. When people extend their time horizons, however, the poor see the possibility of upward mobility, while the rich emphasize future losses, such as unemployment and economic instability. Consistent with the hypotheses, analyses of the 2014 Cooperative Congressional Election Study and a representative Taiwanese dataset from 2016 reveal a clear class cleavage in demand for redistribution among impatient poor and rich respondents, but the cleavage between their patient counterparts diminished. This pattern of convergence extends previous studies on upward mobility and risk perception theory.     

动态视角下中国城市群土地利用TFP测度及影响因素研究

提高城市群土地利用TFP对合理高效利用土地资源,协调好城市群经济发展、资源配置和环境保护具有重大理论和实践意义.文章运用DEA-Malmquist指数对2003—2016年动态视角下中国城市群土地利用TFP进行测度和时空演变分析,并利用Tobit回归模型对影响城市群土地利用TFP变化的因素进行深入研究.结果表明:从时间维度看,城市群土地利用TFP年均上升0.7％,城市群土地利用TFP增长由技术效率和技术进步"双核驱动",技术效率的提升主要与纯技术效率提升有关,规模效率是技术效率和土地利用TFP提升的瓶颈约束;从空间维度看,晋中、哈长、海峡西岸、中原、长江中游、北部湾、成渝、黔中、关中平原和兰西城市群土地利用TFP呈下降趋势,其余城市群土地利用TFP均呈上升趋势,且京津冀、呼包鄂榆、辽中南、长江三角洲、山东半岛和天山北坡城市群土地利用TFP增长为技术进步和技术效率"双核驱动";从影响因素看,人均GDP和年末金融机构人民币各项贷款余额对中国城市群土地利用TFP具有正向促进作用,城市财政支出占本市GDP的比重和人口密度具有负向阻碍作用.     

Efficient heterogeneous sampling for stochastic simulation with an illustration in health care applications

In modeling disease transmission, contacts are assumed to have different infection rates. A proper simulation must model the heterogeneity in the transmission rates. In this article, we present a computationally efficient algorithm that can be applied to a population with heterogeneous transmission rates. We conducted a simulation study to show that the algorithm is more efficient than other algorithms for sampling the disease transmission in a subset of the heterogeneous population. We use a valid stochastic model of pandemic influenza to illustrate the algorithm and to estimate the overall infection attack rates of influenza A (H1N1) in a Canadian city.     

On the mathematics of the Jeffreys–Lindley paradox

This paper is concerned with the well known Jeffreys–Lindley paradox. In a Bayesian set up, the so-called paradox arises when a point null hypothesis is tested and an objective prior is sought for the alternative hypothesis. In particular, the posterior for the null hypothesis tends to one when the uncertainty, i.e., the variance, for the parameter value goes to infinity. We argue that the appropriate way to deal with the paradox is to use simple mathematics, and that any philosophical argument is to be regarded as irrelevant.     

基于动态因子Copula模型的行业间系统性风险分析

国际金融市场间的相关关系以及系统性风险受到很多学者的重视，本文则以我国股市的行业指数作为研究对象进行实证研究。通过构建动态因子Copula模型，文章对行业的日收益率数据进行了动态相关性分析，并基于风险预期占比度量了我国行业之间系统性风险的溢出效应。本文分析了2006年1月4日至2016年7月1日的28个行业指数数据，基于GAS动态负荷因子的变化路径来刻画其相关关系，通过风险预期占比来研究行业间的风险溢出效应。研究表明，各个行业指数收益率之间存在较强的关联性。就单个行业来说，化工行业与其他行业关系最为不稳定。就金融与非金融行业而言，金融行业对非金融行业的影响较大且较为平稳。本文所得研究结果可以为投资者和风险管理者在进行决策时提供一定的指导。     

Fixed T dynamic panel data estimators with multifactor errors

This article analyzes a growing group of fixed T dynamic panel data estimators with a multifactor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels and common observed factors. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit being of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally (iv) the effect of sample size. We apply each of these estimators to a crime application using a panel data set of local government authorities in New South Wales, Australia; we find that the results bear substantially different policy implications relative to those potentially derived from standard dynamic panel GMM estimators. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.     

Mixtures of Gaussian copula factor analyzers for clustering high dimensional data

Mixtures of factor analyzers is a useful model-based clustering method which can avoid the curse of dimensionality in high-dimensional clustering. However, this approach is sensitive to both diverse non-normalities of marginal variables and outliers, which are commonly observed in multivariate experiments. We propose mixtures of Gaussian copula factor analyzers (MGCFA) for clustering high-dimensional clustering. This model has two advantages; (1) it allows different marginal distributions to facilitate fitting flexibility of the mixture model, (2) it can avoid the curse of dimensionality by embedding the factor-analytic structure in the component-correlation matrices of the mixture distribution.An EM algorithm is developed for the fitting of MGCFA. The proposed method is free of the curse of dimensionality and allows any parametric marginal distribution which fits best to the data. It is applied to both synthetic data and a microarray gene expression data for clustering and shows its better performance over several existing methods.     

邓小平社会主义核心价值观教育思想探析

以泰州某大跨度连续梁桥的静动载试验为例，建立ANSYS和桥梁博士模型进行静动力有限元数值计算。将试验结果与模型计算值进行比较，试验结果表明,该桥结构刚度和承载能力满足设计要求。     

Production and changeover control policies of a class of failure prone buffered flow-shops

This article deals with a stochastic optimal control problem for a class of buffered multi-parts flow-shops manufacturing system. The involved machines are subject to random breakdowns and repairs. The flow-shop under consideration is not completely flexible and hence requires setup time and cost in order to switch the production from a part type to another, this changeover is carried on the whole line. Our objective is to find the production plan and the sequence of setups that minimise the cost function, which penalises inventories/backlogs and setups. A continuous dynamic programming formulation of the problem is presented. Then, a numerical scheme is adopted to solve the obtained optimality conditions equations for a two buffered serial machines two parts case. A complete heuristic policy, based on the numerical observations which describe the optimal policies in system states, is developed. It will be shown that the obtained policy is a combination of a KANBAN/CONWIP and a modified hedging corridor policy. Moreover, based on our observations and existent research studies extension to cover more complex flow-shops is henceforth possible. The robustness of such a policy is illustrated through sensitivity analysis.