基于供应链管理的两阶段联合库存S.D模型

运用系统动力学方法构建了基于供应链管理的联合库存S.D模型。首先,在联合库存系统边界优化的基础上,构建了基于供应链管理的两阶段联合库存因果关系图;其次,构造出S.D模型的流图,确切描述出反馈系统的动态性能,界定了流图的主要变量,构造合理的模拟方程和函数,阐述流图中各子模块内部运行机理;最后,结合具体公司的库存管理现状,在模型通过检验的前提下,进行了模型行为预测分析及联合库存模式与传统库存模式之对比分析,从中得出一些较有实际意义的模拟分析结果,为优化库存提供了有益的理论指导。     

Distributions characterized through conditional expectations

Using conditional expectations, we present results that lead to the characterization of several distributions. Both absolutely continuous random variables and discrete random variables are considered. In the case of absolutely continuous random variables, the results lead to the characterization of a family of distributions while in the case of discrete random variables, the distribution is almost uniquely determined under the stated conditions.     

Identifiability criteria for Muth-rational expectations models

The authors deal with complete static linear models that contain current Muth-rational expectations. Rank, order and variety conditions for the identifiability of the structural parameter are derived under general restrictions. We also correct statements that appeared in the literature. Our main finding is that, in general, the standard rank and order conditions are sufficient also for the identifiability of the Muth-rational expectations model parameter, whenever there are enough not fully anticipated exogenous variables. If the number of imperfectly forecasted exogenous variables falls short of the number of endogenous variables by g, then g extra restrictions are needed on every equation and the restrictions must meet easily verifiable variety conditions as well as an augmented rank criterion.     

Corrections for coarsely categorized measures: LISREL's polyserial and polychoric correlations

Versions 5 and 6 of LISREL (Joreskog and Sorbom, 1981) contain procedures that estimate the underlying correlation between continuous variables on the basis of crude rank category measures. The procedures assume that the distribution of the measured variables would have been bivariate normal if they had not been categorized. Using survey data and simulations, the accuracy of these polyserial/polychoric (P/P) based estimates of the underlying correlations are compared with those based on simple equal distance scoring of the categories. The results indicate that under some conditions, e.g., nearly normally distributed variables and moderate to high correlations, the polyserial/polychoric based estimates are better. Under other conditions, e.g., a moderate to high degree of skew and kurtosis and low correlations, the equal distance score based estimates are better. Under all conditions, the amount of error decreases fairly rapidly as the number of categories is increased from two to five.     

MSE-improvement of the least squares estimator by dropping variables

It is well known that dropping variables in regression analysis decreases the variance of the least squares (LS) estimator of the remaining parameters. However, after elimination estimates of these parameters are biased, if the full model is correct. In his recent paper, Boscher (1991) showed that the LS-estimator in the special case of a mean shift model (cf. Cook and Weisberg, 1982) which assumes no “outliers” can be considered in the framework of a linear regression model where some variables are deleted. He derived conditions under which this estimator outperforms the LS-estimator of the full model in terms of the mean squared error (MSE)-matrix criterion. We demonstrate that this approach can be extended to the general set-up of dropping variables. Necessary and sufficient conditions for the MSE-matrix superiority of the LS-estimator in the reduced model over that in the full model are derived. We also provide a uniformly most powerful F-statistic for testing the MSE-improvement.     

One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient,root-<Emphasis Type="Italic">n</Emphasis> consistent StoNEZD method

Understanding the effects of operational conditions and practices on productive efficiency can provide valuable economic and managerial insights. The conventional approach is to use a two-stage method where the efficiency estimates are regressed on contextual variables representing the operational conditions. The main problem of the two-stage approach is that it ignores the correlations between inputs and contextual variables. To address this shortcoming, we build on the recently developed regression interpretation of data envelopment analysis (DEA) to develop a new one-stage semi-nonparametric estimator that combines the nonparametric DEA-style frontier with a regression model of the contextual variables. The new method is referred to as stochastic semi-nonparametric envelopment of z variables data (StoNEZD). The StoNEZD estimator for the contextual variables is shown to be statistically consistent under less restrictive assumptions than those required by the two-stage DEA estimator. Further, the StoNEZD estimator is shown to be unbiased, asymptotically efficient, asymptotically normally distributed, and converge at the standard parametric rate of order n ^−1/2. Therefore, the conventional methods of statistical testing and confidence intervals apply for asymptotic inference. Finite sample performance of the proposed estimators is examined through Monte Carlo simulations.     

Identification with latent variables

This is an essay on a unified approach to the identifiability problem in static models with and without hidden endogenous variables. As is well known, when some of these variables are unobserved, the prior information requirements for models when all endogenous variables are observed, are still there. In addition, extra prior information that takes the place of the means and covariances of the missing variables will have to be supplied directly or indirectly by the statistical researcher. In the paper we characterize the quality and quantity of the required information for the general linear static model and apply it when the model is i) an econometric demand and supply model with missing observations on the quantity transacted, ii) a factor analysis model with observed characteristics of the test takers and iii) a LISREL Model without fixed exogenous variables. With unknown true parameters, the exact rank conditions are seldom verifiable but we do recommend an implementable check-list that is adequate for almost all parameters.     

Identification of rational expectations models

This paper deals with the problem of the identification of simultaneous Rational Expectations (RE) models. In the case of RE models with current expectations of the endogenous variables, the necessary and sufficient conditions for the global identification are derived explicitly in terms of the structural parameters and the linear homogenous identifying restrictions. It is shown that in the absence of a priori restrictions on the processes generating the exogenous variables and the disturbances, RE models and general distributed lag models are ‘observationally equivalent’. In the case of RE models with future expectations of the endogenous variables, a general solution that highlights the ‘non-uniqueness’ problem and from which other solutions such as forward or backward solutions can be obtained, is derived. It is shown that untestable and often quite arbitrary restrictions are needed if RE models with future expectations are to be identifiable. Certain order conditions similar to those obtained for the identification of RE models with current expectations are also derived for this case.     

Identification of simultaneous equation models with measurement error

This paper establishes identification conditions for a simultaneous equation model in which some of the exogenous variables are measured with error. It is assumed that observational information is confined to the covariance matrix of the observed variables and that prior information on the structural coefficients and error variances takes the form of zero restrictions. The primary result is an easily-applied assignment condition for checking whether or not there are an adequate number and variety of prior restrictions to identify the structural parameters.     

基于人工神经网络与主成分分析的建筑工料(工日)估算方法

针对建筑工料(工日)估算问题,文章首先利用主成分分析得到一组新的输入变量,相对于原始输入变量,有效降低了输入维数,且消除了各输入分量之间的相关性,然后以新的输入变量作为改进型BP网络的输入进行训练与估算,得到了一种新的工料(工日)估算方法。仿真结果表明与直接利用BP网络训练估算相比较,采用这种估算方法,估算结果更加准确.     

The Indian perspective of strategic HR roles and organizational learning capability

This study focuses on the empirical analysis of strategic HR roles and organizational learning capability. Line and HR firm performance is further analysed. The sample size consisted of 640 managers in India. Standardized questionnaires were used as tools for the managers' perception of the two variables and their link to data collection. Statistical results indicate that correlation coefficients were mostly significant and positive for the variables and sub-variables of strategic HR roles and organizational learning capability. Discriminant functional analysis reflected that line and HR managers differed significantly in their perception of both variables. Stepwise regression analysis indicated that both the variables of strategic HR roles and organizational learning capability predict firm performance.     

Asymptotic properties of random extremes under general normalization from nonidentical distributions

In this paper we study the weak convergence of the generally normalized extremes (extremes under nonlinear monotone normalization) of random number of independent (nonidentically distributed) random variables. When the random sample size is assumed to converge in probability and the interrelation between the basic variables and their random size is not restricted, the limit forms as well as the sufficient conditions of convergence are derived. Moreover, when the random sample size is assumed to converge weakly and independent of the basic variables, the necessary and sufficient conditions for the convergence are derived.Acknowledgment. The authors are grateful to a referee for several helpful comments and for pointing out the extensive paper by Galambos (1992) leading to improvement of the representation of the paper.Received January 2002     

Consistent selection of explanatory variables

We consider a linear regression model where some explanatory variables are unknown members of sets of alternative explanatory variables. It will be shown that under weak conditions the minimum residual variance criterion for selecting these explanatory variables has the property that the probability of selecting wrong explanatory variables vanishes if the number of observations increases to infmity. Moreover, the O.L.S. estimator of the resulting "specified" model turns out to be consistent, while in the case that all the parameters are nonzero it can be shown that this O.L.S. estimator has the same limiting distribution as the O.L.S. estimator of the true model.     

Perceptions of On- The-Job Discrimination and Employees With Disabilities

This study examines factors that predict perceptions of workplace discrimination by employees with disabilities. Individual level variables are combined with organizational level variables in a single model of perceived inequality. Data came from surveys administered to employees with disabilities and their respective employers. Individual and organizational variables together provide a better understanding of perceived discrimination than either set alone. Despite the predominance of studies that demonstrate inequality in compensation, this study shows that employees experience discrimination over most terms/conditions of employment.     

Estimating dynamic models from repeated cross-sections

An important feature of panel data is that it allows the estimation of parameters characterizing dynamics from individual level data. Several authors argue that such parameters can also be identified from repeated cross-section data and present estimators to do so. This paper reviews the identification conditions underlying these estimators. As grouping data to obtain a pseudo-panel is an application of instrumental variables (IV), identification requires that standard IV conditions are met. This paper explicitly discusses the implications of these conditions for empirical analyses. We also propose a computationally attractive IV estimator that is consistent under essentially the same conditions as existing estimators. While a Monte Carlo study indicates that this estimator may work well under relatively weak conditions, these conditions are not trivially satisfied in applied work. Accordingly, a key conclusion of the paper is that these estimators cannot be implemented under general conditions.     

自相关检验方法的检验功效研究

研究目标:探究DW检验和LM检验的检验功效及其渐近性。研究方法:运用蒙特卡罗模拟实验方法结合相关影响因素对两种检验方法进行分析与比较。影响因素包括样本容量、解释变量的随机性及自相关性、随机误差项的自相关程度以及分布形态。研究发现:DW和LM检验功效与样本容量和随机误差项的自相关程度正相关,与解释变量的自相关程度负相关;解释变量的随机性对DW和LM检验功效无显著影响;误差项的几种常见分布形态的变化对DW和LM检验功效的影响可以忽略;在误差项存在一阶自相关的情况下,DW检验效果优于LM检验效果。研究创新:以DW检验和LM检验的假设条件为出发点,探究比较不同条件下自相关检验方法的检验功效。研究价值:在实证研究背景下为有效地选择自相关检验方法提供借鉴参考。     

Modeling Criminal Activity in an Area in Economic Decline: Local Economic Conditions Are a Major Factor in Local Property Crimes

A bstract . A multi-factor model includes economic, apprehension, seasonal a n d plant closing variables as the explanatory regressors and crimes against property as the dependent variable. Different lag structures were used on the explanatory variables such as an Almon distributed lag of a second degree polynominal nature and the lagging of the dependent variable by one quarter so that the model would more closely approximate the environment being considered. The results suggest a definite seasonal pattern in crimes against property, and the economic variables measuring local, not national, conditions, appear to be more significant regressors than any other explanatory variables.     

THE DISTRIBUTION OF GENERAL QUADRATIC FORMS IN NORMA

For a general quadratic form in normal variables a representation in terms of independently distributed standard normal variables is derived. The necessary and sufficient conditions for such a quadratic form to have a non–central chi–squared distribution can be found easily using this representation.     

Bias of OLS Estimators due to Exclusion of Relevant Variables and Inclusion of Irrelevant Variables

In this paper, I discuss three issues related to bias of OLS estimators in a general multivariate setting. First, I discuss the bias that arises from omitting relevant variables. I offer a geometric interpretation of such bias and derive sufficient conditions in terms of sign restrictions that allows us to determine the direction of bias. Second, I show that inclusion of some omitted variables will not necessarily reduce the magnitude of bias as long as some others remain omitted. Third, I show that inclusion of irrelevant variables in a model with omitted variables can also have an impact on the bias of OLS estimators. I use a running example of a simple wage regression to illustrate my arguments.