构成型模型在用户满意度测量模型中的应用

本文研究构成型外部关系模型在用户满意度测量模型中的应用问题。作者分析了常见用户满意度测量模型中各个结构变量的外部关系,说明模型中某些结构变量符合构成型模型的要求。作者进一步将预期质量和感知质量的外部关系定义为构成型模型,并分析实际数据。结果说明,针对用户满意度测量模型中的某些结构变量采用构成型模型是合理的,构成型模型可以比反映型模型提供更多的信息。     

基于第一HJ距离的线性因子模型设定检验

模型设定检验是金融建模的重要环节,是减少模型风险的关键步骤.本文基于Hansen和Jagannathan[1]提出的第一HJ距离模型误设测度,以台湾市场丰富的股票和指数期权数据为基础,对8种常见的线性因子模型(包括基于金融资产价格的线性因子模型)进行模型误设检验,并探究模型设定对参数检验的影响.研究发现:在5%的显著性水平下,所有无条件信息模型均存在模型误设问题,仅FF3、LM、VanM、SkewM的条件信息模型成为可接受的正确模型;同时,是否考虑模型可能误设会影响SDF参数的检验,考虑模型可能误设能更有效地侦测因子的定价能力,而不考虑模型可能误设会高估模型SDF参数的t值绝对值,致使部分因子可能存在"伪"定价现象.     

基于MCMC模拟的期货最优套保比贝叶斯分析

计量经济模型总风险由模型(误设)风险和估计风险构成.本文同时运用基于频率统计的后视计量经济模型和基于McMC模拟的贝叶斯方法对我国铜期货市场不同套期保值期限的最优套期保值比进行实证分析.实证结果清楚表明,估计风险对模型结果有重要影响.在处理估计风险方面,贝叶斯方法较频率统计方法有明显优势.另外,套期保值效率与套期保值期限之间的正相关关系在本研究中得到确定,无论是基于频率统计,还是基于贝叶斯统计.     

模型的复杂性与期货套期保值效率:基于环境突变样本区间的检验

本文从模型风险的角度,选取环境突变样本区间,对“模型的复杂性和期货套期保值效率”这一问题进行研究.采用随机系数马尔科夫体制转换(RCMRS)模型对期货最优套期保值比进行估计.并将RCMRS模型套期保值效率和OLS、VAR、VECM及GARCH等模型进行比较和分析.样本内比较发现,复杂的动态模型并未带来明显优于静态模型的套期保值表现.样本外比较则显示,动态模型的套期保值效率明显劣于静态模型.当环境突变,模型存在明显的误设时,由于复杂模型较简单模型涉及到更多变量和假定,模型(误设)风险较简单模型更大.再加上复杂模型较简单模型包含更多噪音,估计风险更大,综合来看,复杂模型的总风险明显大于简单模型,这直接导致复杂模型的套期保值效率劣于简单模型.     

社会和谐指数时空评判模型及其实证分析

社会的发展进步与自然的友好相处是社会和谐的客观表现。以人为本的社会主体对社会的满意程度是社会和谐的主观体现,两者构成了和谐社会评价的软硬指标。本文基于“相对差值比较”提出的社会和谐指数时空评判模型可以主客观的全方位衡量社会的和谐程度．该模型可以通过客观指标来反映社会的和谐程度以及人们对社会的满意程度。应用此模型进行了全国6个城市2004年的社会和谐指数测量的实证分析。     

虚拟世界网络用户生活方式测量模型研究

近年来,以"第二人生"为代表的虚拟世界在互联网应用领域所产生的影响及其蕴含的巨大商机已经引起企业界的高度重视.然而,关于虚拟世界的理论研究目前仅处于起步阶段,尚缺乏对虚拟世界用户生活方式等行为规律系统深入的认识,可供借鉴的研究方法和测量工具也很有限.基于现有的关于现实世界消费者生活方式和网络用户生活方式的研究,提出虚拟世界生活方式的概念及其测量模型,并采用在"第二人生"中进行街头拦访和问卷调查的研究方式荻取数据,对该模型进行量表设计和实证分析;通过探索性因子分析和验证性因子分析,初步建立包含个性满足、人际交往、社会融入和虚拟体验4个维度的虚拟世界生活方式测量模型,用以综合反映虚拟世界居民的行为、兴趣、价值观和态度等基本特征.为今后关于虚拟世界居民生活方式的研究与应用提供可以借鉴的测量工具,同时也表明在虚拟世界进行街头拦访这一实证研究方法具有可行性.     

中国人面子观在RM和FM模型中的测量差异及其对绿色产品偏好的影响研究北大核心CSSCI

为了更好了解"面子"的一般性内涵和情景性内涵,重点研究了反映性测量模型(RM)和形成性测量模型(FM)在测量"面子"构念上的差异,以及不同测量模型的稳定性和对"面子"变量的解释能力。通过系列研究,证实了面子的反应性测量模型具有较好的信效度和稳定性;而形成性测量模型能更好地解释面子的文化内涵。同时,以此为基础进一步探索了"面子"内涵对绿色消费行为的影响,结果发现,道德型面子观较强的消费者具有积极的亲社会消费动机,从而对绿色产品产生更强的偏好;而社会型面子观较强的消费者则对不具有亲社会属性的奢侈品产生了更强的偏好。     

顾客导向的高速公路服务质量测量体系探索性研究

本文以服务管理理论为理论背景,从顾客导向的角度出发,分析我国高速公路服务的属性和特点,从高速公路关键服务接触与服务内容入手,深入分析高速公路服务质量的属性和构成要素,开发顾客导向高速公路服务质量测量指标,构建高速公路顾客导向服务质量测量评价模型,并对该模型进行实证验证。研究成果拓展和丰富了服务管理理论的应用领域,对高速公路服务质量测量这一具有中国特色的服务管理研究领域进行了有益的探索。     

“黑板管理学”的3个来源——操作主义视角下管理理论与实践脱节问题分析

在国内外学术界,管理学研究都出现了某种程度的"黑板化"倾向.尽管关于管理理论与实践脱节的问题比较复杂,但其本质上是一个研究方法论问题.从布里奇曼的基于操作主义的实证研究方法论的视角来看,可以将管理理论与实践脱节的"黑板管理学"的来源总结为以下3个原因:概念没有很好定义导致无法测量的"泛量表式研究"、概念分解中的"伪还原"以及忽视研究结论可操作性的"管理解释学".     

基于第一 HJ 距离的线性因子模型设定检验

模型设定检验是金融建模的重要环节,是减少模型风险的关键步骤． 本文基于 Hansen和 Jagannathan 提出的第一 HJ 距离模型误设测度 以台湾市场丰富的股票和指数期权数据为基础,对 8 种常见的线性因子模型( 包括基于金融资产价格的线性因子模型) 进行模型误设检验,并探究模型设定对参数检验的影响． 研究发现: 在 5% 的显著性水平下,所有无条件信息模型均存在模型误设问题,仅 FF3、LM、VanM、SkewM 的条件信息模型成为可接受的正确模型; 同时,是否考虑模型可能误设会影响 SDF 参数的检验,考虑模型可能误设能更有效地侦测因子的定价能力,而不考虑模型可能误设会高估模型 SDF 参数的 t 值绝对值,致使部分因子可能存在“伪”定价现象．     

The mismeasure of man(agement) and its implications for leadership research

The purpose of the present review is to examine the potential problems caused by measurement model misspecification in the field of leadership. First, we discuss the conceptual differences between the four types of measurement models that have been used in leadership research and provide a set of criteria that could be used to decide upon the appropriate model. Following this, we examine the extent of measurement model misspecification by conducting a review of the leadership literature and applying these criteria. Next, we discuss the potential consequences of measurement model misspecification on conclusions made about the structural relationships between constructs. Finally, we discuss the implications of our study for leadership research.     

考虑微观结构噪声与测量误差的波动率预测

以测量误差的分布理论为基础，本文将微观结构噪声的影响引入到测量误差的方差中，构建了包含微观结构噪声影响的HARQ-N模型。使用蒙特卡洛模拟与中国股市的高频数据对HAR、HARQ、HARQ-N模型与HAR-RV-N-CJ模型的估计和预测进行了比较，研究发现，HARQ模型和HARQ-N模型的测量误差修正项对波动率的影响系数统计显著为负，HARQ-N模型的测量误差项影响系数远大于HARQ模型，更大程度地减弱当期微观结构噪声和测量误差的影响。并且，考虑微观结构噪声和测量误差的HARQ-N模型样本内和样本外预测效果在统计上显著优于HAR模型、HARQ模型与HAR-RV-N-CJ模型。     

中国沪深股市结构性波动的政策性影响因素

本文利用划分均值和方差变点的迭代累积平方和算法(ICSS:MV)对上证综指和深证成指1996年12月16日至2010年12月31日的日收益率序列进行结构变点的检验,通过将结构变点与重大事件对应选取影响沪深股市结构性波动的政策性事件,并根据选取的事件将样本区间分成13个子区间。为了避免参数模型中模型误设的缺陷,利用非参数GARCH模型估计样本区间的波动率;最后利用N-W核回归估计对非参数GARCH估计的波动率与收益率进行回归,分析股市结构性波动产生的政策性影响因素。通过分析发现央行调整存贷款基准利率和存款准备金率、国有股的减持、允许保险公司等机构投资者买卖证券投资基金、调整印花税等政策性因素是造成我国股市变结构波动的重要原因。     

测量系统的质量监控技术

在制造业中,测量系统是保证和提高产品质量的必需技术手段。本文提出了将统计过程控制(SPC)技术应用于监控测量过程的思想,首先指出了测量过程与一般生产过程之间的差异,并建立了测量过程的一般模型;在此基础上,引入了监控测量过程的同分布原则和实现技术,并给出了实证分析。     

Effort-reward imbalance and work-home interference: a two-wave study among European male nurses

ABSTRACT

This one-year follow-up study among 1,421 male nurses from seven European countries tested the validity of the Effort-Reward Imbalance (ERI) model in predicting prospective vital exhaustion and work-home interference. We hypothesised that effort and lack of reward would have both main and interactive effects on future outcomes. Results of structural equation modelling (SEM) showed that effort was positively related to exhaustion and work-home interference, both simultaneously and over time. Lack of reward predicted increased exhaustion at follow-up, but effort-reward imbalance did not influence the outcomes. Additionally, Time 1 exhaustion predicted increased work-home interference and exhaustion at follow-up. These results do not support the ERI model, which postulates a primacy of effort-reward imbalance over main effects. Instead, the findings are in line with dual path models of job stress and work-home interference. Multi-group SEM showed partial cross-cultural metric invariance for the ERI measure of effort, but the ERI measure of rewards showed no metric measurement invariance, indicating its meaning is qualitatively different across cultures. Nevertheless, the main conclusions were markedly similar for each national sub-sample. We discuss the theoretical and practical implications of our study.     

12 Structural Equation Modeling in Management Research: A Guide for Improved Analysis

Abstract

A large segment of management research in recent years has used structural equation modeling (SEM) as an analytical approach that simultaneously combines factor analysis and linear regression models for theory testing. With this approach, latent variables (factors) represent the concepts of a theory, and data from measures (indicators) are used as input for statistical analyses that provide evidence about the relationships among latent variables. This chapter first provides a brief introduction to SEM and its concepts and terminology. We then discuss four issues related to the measurement component of such models, including how indicators are developed, types of relationships between indicators and latent variables, approaches for multidimensional constructs, and analyses needed when data from multiple time points or multiple groups are examined. In our second major section, we focus on six issues related to the structural component of structural equation models, including how to examine mediation and moderation, dealing with longitudinal and multilevel data, issues related to the use of control variables, and judging the adequacy of models and latent variable relationships. We conclude with a set of recommendations for how future applications of SEM in management research can be improved.     

Spurious Inference in Reduced‐Rank Asset‐Pricing Models

This note studies some seemingly anomalous results that arise in possibly misspecified, reduced‐rank linear asset‐pricing models estimated by the continuously updated generalized method of moments. When a spurious factor (that is, a factor that is uncorrelated with the returns on the test assets) is present, the test for correct model specification has asymptotic power that is equal to the nominal size. In other words, applied researchers will erroneously conclude that the model is correctly specified even when the degree of misspecification is arbitrarily large. The rejection probability of the test for overidentifying restrictions typically decreases further in underidentified models where the dimension of the null space is larger than 1.     

Methods for Detecting and Estimating Population Threshold Concentrations for Air Pollution–Related Mortality with Exposure Measurement Error

The association between daily fluctuations in ambient particulate matter and daily variations in nonaccidental mortality have been extensively investigated. Although it is now widely recognized that such an association exists, the form of the concentration–response model is still in question. Linear, no threshold and linear threshold models have been most commonly examined. In this paper we considered methods to detect and estimate threshold concentrations using time series data of daily mortality rates and air pollution concentrations. Because exposure is measured with error, we also considered the influence of measurement error in distinguishing between these two completing model specifications. The methods were illustrated on a 15-year daily time series of nonaccidental mortality and particulate air pollution data in Toronto, Canada. Nonparametric smoothed representations of the association between mortality and air pollution were adequate to graphically distinguish between these two forms. Weighted nonlinear regression methods for relative risk models were adequate to give nearly unbiased estimates of threshold concentrations even under conditions of extreme exposure measurement error. The uncertainty in the threshold estimates increased with the degree of exposure error. Regression models incorporating threshold concentrations could be clearly distinguished from linear relative risk models in the presence of exposure measurement error. The assumption of a linear model given that a threshold model was the correct form usually resulted in overestimates in the number of averted premature deaths, except for low threshold concentrations and large measurement error.     

稳健非参数VaR建模及风险量化研究

参数VaR模型被广泛应用于风险测量中,然而需要给出具体的结构形式,这就容易发生模型错误设定的灾难,使风险计量的精确性易于产生较大偏差。针对参数VaR模型的设定误差问题,本文构建了SQ-ARCH和Nop-Quantile两个非参数VaR模型,诣在提高传统风险计量模型的灵活性、稳定性和准确性。采用稳健的分位数回归方法,得到了计算这两个VaR模型的具体表达式并给出了模型估计的算法和步骤。Monte Carlo模拟发现无论模型正确还是错误设定非参数VaR模型比参数ARCH类VaR模型更稳健。此外,把这两个稳健非参数VaR模型应用于我国股票市场风险量化的实证分析中。研究结果表明稳健非参数VaR模型比参数ARCH类VaR模型度量风险更准确。