人力资源管理胜任能力构成因素实证研究

人力资源管理胜任能力研究是组织对人力资源从业者进行管理和开发的基础,而目前中国在该领域的研究较少,且尚无形成一致的认识。鉴此,根据国内外人力资源管理胜任能力相关研究成果,结合专家访谈设计的人力资源从业者胜任素质构成问卷调研结果,运用因子分析法进行数据处理并对人力资源管理工作胜任能力的构成因素进行分析识别,构建了人力资源管理胜任能力模型,并应用结构方程模型研究各构成因素对其胜任能力的影响程度得出了相关研究结论。     

公德和私德:企业家道德行为的内容结构及测量

文章以企业家道德行为为研究对象,具体研究企业家道德行为的内容结构.将企业家道德行为分为公德和私德两个构面.遵循量表开发的程序和方法,开发了相关量表.结果表明,公德量表由一个因子构成,私德量表由五个因子构成,各维因子量表均具有较高的信度和效度.     

基于自适应组LASSO估计的高维门限因子模型一致选择

门限因子模型设定载荷具有阈值型区制转换结构,可以同时刻画高维时间序列的共变性和区制转换特征。针对高维门限因子模型,本文基于自适应组LASSO技术给出了一种一致模型选择过程。这一模型选择过程将因子个数设定、门限效应推断纳入统一的分析框架,不仅解决了模型选择的一致性问题,还同时实现了模型选择误差的统一控制,这对于高维门限因子模型而言是非常重要的。理论研究和随机模拟结论表明本文给出的一致模型选择过程具有良好的大样本性质和有限样本表现。最后,本文将门限因子模型应用于我国金融市场分析,实证结果进一步验证了本文理论的有效性。     

基于SUR的商业银行信用风险宏观压力测试研究

世界危机后,压力测试作为风险管理的一种新手段,倍受关注.而信用风险宏观压力测试研究在全球范围已成为具有挑战性的课题之一.文章分析了商业银行信用风险的宏观经济压力因素,建立了压力指标体系并据此假设计压力情景;采用Logit回归和向量自回归构成表面似无关方程组构建了压力测试模型.     

农机制造业人力资源管理者胜任素质模型构建

文章运用行为事件访谈法,提取农机制造业人力资源管理者胜任素质初始条目,运用因子分析法对初始质条目进行筛选和优化,确立了由胜任“技术”维、胜任“能力”维、胜任“知识”维和胜任“特质”维这四个因子构成的人力资源管理者胜任素质模型,提出基于该模型的职位分析、培训、绩效考核和薪酬管理.     

主观幸福的测量模型

主观幸福的测量一直是该领域研究的热点与难点.文章通过结构方程技术,建立了主观幸福的测量模型.该模型显示主观幸福是由生活满意度、自我满意度、积极情感和消极情感四种主要成分构成的分层结构,并揭示了这四种主要成分与其指标,各成分之间,以及它们与主观幸福之间的内在关系,验证了关于主观幸福内涵的界定.     

油气开发的实物期权特征及应用B-S模型的条件检验

分析石油开发期权特征以及应用B-S模型的假设条件,并使用SPSS软件的K-S检验以及Q-Q图分析方法,通过对纽约伦敦两大石油交易所原油价格数据的研究,验证了石油价格遵循对数正态分布的假设,从而检验了石油开发期权应用B-S模型评价中几何布朗运动的前提条件假设。     

工资与物价会螺旋波动吗？

VECM模型分析表明,我国工资与物价间存在明显的螺旋波动关系。将工资进一步分解为城镇劳动力工资和农民工工资后发现,这一螺旋关系是由城镇劳动力工资与物价之间的直接螺旋关系以及农民工工资与物价之间的间接螺旋关系组合而成的。城镇劳动力的工资波动会对物价构成直接压力,而农民工工资波动会通过城镇劳动力工资对物价构成间接压力。文章进一步从二元就业制度下劳动供给机制扭曲的角度分析了两类螺旋关系的原因。     

区域性房地产业投资系统的构形演化

房地产业的发展对拉动GDP的增长起着重要作用,而房地产投资问题是我国房地产市场发展过程中最重要的问题之一.在一定区域内所有的房地产投资企业构成了房地产投资系统,这是一个复杂系统.文章将哈肯学派WeidliSh W & Hagg G的投资经济系统模型引入房地产投资系统中,对其中的参数赋予新的含义和内容,并对互变因子引入时间参数,进而运用模型和复杂系统理论研究区域性房地产投资系统的构形演化.     

企业技能人才能力模型的构建

文章从研究技能人才的能力构成及构成技能人才能力的要素入手,构建技能人才六个维度的能力模型,并基于技能人才能力模型,从能力评价、能力开发培养、能力的激励及能力的使用等四个方面,提出完善技能人才能力建设机制的对策。     

A deflated indicators approach for estimating second-order reflective models through PLS-PM: an empirical illustration

The paper provides a procedure aimed at obtaining more interpretable second-order models estimated with the partial least squares-path modeling. Advantages in interpretation stem from the separation of the two sources of influence on the data. As a matter of fact, in hierarchical models effects on manifest variables (MVs) are assigned to both first-order (specific) factors and second-order (general) factors. In order to separate these overlapping contributions, MVs are deflated from the effect of the specific latent variables (LVs) and used as indicators of the second-order LV. A case study is presented in order to illustrate the application of the proposed method.     

Non Parametric Estimation of Second-Order Diffusion Equation by Using Asymmetric Kernels

In this paper, we study the non parametric estimation of drift coefficient and diffusion coefficient in the second-order diffusion equation by using the asymmetric kernel functions, based on the difference of discrete time observations. The basic idea relies upon replacing the symmetric kernel by asymmetric kernel and provides a new way of obtaining the non parametric estimation for second-order diffusion equation. Under the appropriate assumptions, we prove that the proposed estimators of second-order diffusion equation are consistent and asymptotically follow normal distribution.     

On Linear Operations on Stationary and Non Stationary Random Processes

This brief article considers an arbitrary linear operation on stationary and non stationary second-order random processes and provides conditions for the output process to be of second order. Both mean-square integrals and sample-path integrals are considered.     

Analytical Characterization of the Information Matrix for Split-Plot Designs

This article provides the analytical characterization of the inverse of the information matrix for second-order SPD. A particular feature of these explicit expressions is that they are functions of the design parameters enabling the development of analytical functions to efficiently compute exact design optimality criteria. The application of these analytical expressions is demonstrated using the generalized variance of the parameter estimates for second-order SPD. An example illustrating the use of these expressions is also presented.     

Gaussian process emulation for second-order Monte Carlo simulations

We consider the use of emulator technology as an alternative method to second-order Monte Carlo (2DMC) in the uncertainty analysis for a percentile from the output of a stochastic model. 2DMC is a technique that uses repeated sampling in order to make inferences on the uncertainty and variability in a model output. The conventional 2DMC approach can often be highly computational, making methods for uncertainty and sensitivity analysis unfeasible. We explore the adequacy and efficiency of the emulation approach, and we find that emulation provides a viable alternative in this situation. We demonstrate these methods using two different examples of different input dimensions, including an application that considers contamination in pre-pasteurised milk.     

Second-order asymptotic comparison of estimators under φ-divergence loss

Our main concern is on the second-order asymptotic optimality problem of estimators. The φ-divergence loss is used as a criterion for evaluating the performance of estimators. In the comparison problem of any two estimators, the condition that one estimator dominates another estimator under the φ-divergence risk is given by evaluating the second-order term in the difference between the risks. As a result, it is proved that the condition is characterized by a peculiar value of the φ-divergence loss, which is called the divergence-loss coefficient. Furthermore, it is shown that the comparison based on the φ-divergence loss does not correspond with that based on any standard loss functions including the mean squared error, the absolute loss and the 0-1 loss. In addition, a necessary and sufficient condition for an estimator to be second-order admissible is derived.     

Simulating Properties of the Likelihood Ratio Test for a Unit Root in an Explosive Second-Order Autoregression

This paper provides a means of accurately simulating explosive autoregressive processes and uses this method to analyze the distribution of the likelihood ratio test statistic for an explosive second-order autoregressive process of a unit root. While the standard Dickey-Fuller distribution is known to apply in this case, simulations of statistics in the explosive region are beset by the magnitude of the numbers involved, which cause numerical inaccuracies. This has previously constituted a bar on supporting asymptotic results by means of simulation, and analyzing the finite sample properties of tests in the explosive region.     

Using the Hybrid Two-Step estimation approach for the identification of second-order latent variable models

The aim of this paper is to define a new approach, called Hybrid Two-Step, to estimate the parameters of a second-order latent variable (LV) model in the case of formative relationships between the first-order and the second-order LVs. In this respect, we introduce the two main approaches to the estimation of second-order constructs through the partial least squares-path modelling: the so-called Repeated Indicators approach and the Two-Step approach. Some criticisms of these methodologies are highlighted and a solution to the issue of the identification of formative second-order constructs is suggested through the adoption of a Hybrid Two-Step approach. A Monte Carlo simulation study aimed at comparing the approach proposed with the traditional ones was performed. Finally, a case study about the passenger satisfaction is presented to show the implementation of the method and to give some comparative empirical results.     

Simulating Properties of the Likelihood Ratio Test for a Unit Root in an Explosive Second-Order Autoregression

This paper provides a means of accurately simulating explosive autoregressive processes and uses this method to analyze the distribution of the likelihood ratio test statistic for an explosive second-order autoregressive process of a unit root. While the standard Dickey–Fuller distribution is known to apply in this case, simulations of statistics in the explosive region are beset by the magnitude of the numbers involved, which cause numerical inaccuracies. This has previously constituted a bar on supporting asymptotic results by means of simulation, and analyzing the finite sample properties of tests in the explosive region.     

Factor Stochastic Volatility in Mean Models: A GMM Approach

This paper provides a semiparametric framework for modeling multivariate conditional heteroskedasticity. We put forward latent stochastic volatility (SV) factors as capturing the commonality in the joint conditional variance matrix of asset returns. This approach is in line with common features as studied by Engle and Kozicki (1993), and it allows us to focus on identication of factors and factor loadings through first- and second-order conditional moments only. We assume that the time-varying part of risk premiums is based on constant prices of factor risks, and we consider a factor SV in mean model. Additional specification of both expectations and volatility of future volatility of factors provides conditional moment restrictions, through which the parameters of the model are all identied. These conditional moment restrictions pave the way for instrumental variables estimation and GMM inference.