非对称信息条件下成本控制模型研究

运用委托代理理论方法探讨了信息不对称条件下,生产管理者如何激励生产者努力降低成本的问题。信息不对称情况下,管理者不能观测到生产者为降低成本而付出的努力,因此管理者需要对生产者进行激励。管理者根据自己的要求和生产者的自报数确定了一个合约基数,并建立了满足生产者个体理性约束和激励相容约束条件下的管理者期望效用最大化模型,分别以奖励系数和惩罚系数为决策主量。运用进化规划算法作了仿真计算。     

公司并购绩效与公司边界:交易费用的视角

公司并购重组是重新确定公司边界的一种有效机制。本文充分整合产权理论、交易费用经济学、影响成本理论以及公司并购协同效应理论,推导出影响公司边界的若干因素,确定出影响公司并购绩效的主要潜在因素,再在此基础上建立一个分析公司并购绩效与公司边界的综合性理论框架。结果发现,资产专用性、影响成本与公司并购协同效应都将对公司边界的调整和公司并购绩效产生决定性的影响,而且这个影响的大小又取决于治理结构、融资工具以及组织结构等诸多因素。     

基于时间学的思想政治教育时间规律新探

随着即时网络时代的到来，思想政治教育学要实现现代转型，探究者必须紧跟时代行进的步履，发扬探赜索隐、钩深致远的精神，深入揭示和科学应用思想政治教育新规律尤其是时间规律。基于时间学，从社会背景条件、教育对象、教育者、管理者角度去考察，思想政治教育时间规律群大致包括思想政治教育一定要适合时代发展状况规律、思想政治品德形成发展的时间规律、对人们进行思想政治教育的时间规律和思想政治教育时间管理规律等四大类型的一级时间规律。其中，每一类型的一级规律包含诸多二级规律或曰子规律。自觉把握和科学应用这些规律，有助于推动思想政治教育不断从实然状态向应然状态、从必然王国向自由王国飞跃。     

Parameter Estimation of Autoregressive Models Using the Iteratively Robust Filtered Fast-τ Method

Utilizing time series modeling entails estimating the model parameters and dispersion. Classical estimators for autocorrelated observations are sensitive to presence of different types of outliers and lead to bias estimation and misinterpretation. It is important to present robust methods for parameters estimation which are not influenced by contaminations. In this article, an estimation method entitled Iteratively Robust Filtered Fast? τ(IRFFT) is proposed for general autoregressive models. In comparison to other commonly accepted methods, this method is more efficient and has lower sensitivity to contaminations due to having desirable robustness properties. This has been demonstrated by applying MSE, influence function, and breakdown point criteria.     

Trend smoothness achieved by penalized least squares with the smoothing parameter chosen by optimality criteria

This work presents a study about the smoothness attained by the methods more frequently used to choose the smoothing parameter in the context of splines: Cross Validation, Generalized Cross Validation, and corrected Akaike and Bayesian Information Criteria, implemented with Penalized Least Squares. It is concluded that the amount of smoothness strongly depends on the length of the series and on the type of underlying trend, while the presence of seasonality even though statistically significant is less relevant. The intrinsic variability of the series is not statistically significant and its effect is taken into account only through the smoothing parameter.     

Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models

Time-varying parameter models with stochastic volatility are widely used to study macroeconomic and financial data. These models are almost exclusively estimated using Bayesian methods. A common practice is to focus on prior distributions that themselves depend on relatively few hyperparameters such as the scaling factor for the prior covariance matrix of the residuals governing time variation in the parameters. The choice of these hyperparameters is crucial because their influence is sizeable for standard sample sizes. In this article, we treat the hyperparameters as part of a hierarchical model and propose a fast, tractable, easy-to-implement, and fully Bayesian approach to estimate those hyperparameters jointly with all other parameters in the model. We show via Monte Carlo simulations that, in this class of models, our approach can drastically improve on using fixed hyperparameters previously proposed in the literature. Supplementary materials for this article are available online.     

Time‐Space Distanciation: An Interdisciplinary Account of How Culture Shapes the Implicit and Explicit Psychology of Time and Space

The growing body of research on temporal and spatial experience lacks a comprehensive theoretical approach. Drawing on Giddens’ framework, we present time‐space distanciation (TSD) as a construct for theorizing the relations between culture, time, and space. TSD in a culture may be understood as the extent to which (1) time and space are abstracted as separate dimensions and (2) activities are extended and organized across time and space. After providing a historical account of its development, we outline a multi‐level conceptualization of TSD supported by research on cultural differences in the experience of time and space. We impact this conceptualization by examining two ethnographic case studies. We conclude by highlighting future research directions. TSD is an integrative, interdisciplinary, multi level construct with the potential to guide the burgeoning social science of time and space.     

Institutionalising the evaluation function: A South African study of impartiality,use and cost

PurposeThis article explores the implications of outsourcing the evaluation function in South Africa, a context where there is a mismatch between evaluation supply and demand. It unpacks the tradeoffs between internal and external evaluation, and challenges some commonly held assumptions about both.ApproachBased on experiences as an internal evaluator, external evaluator, evaluation manager, and building evaluation capacity, the author explores how each role changes when evaluation is a scarce skill, and looks at implications outsourcing has for both the organization, and the evaluation.FindingsThe purpose of the evaluation must drive the decision to outsource. However, with changing models of collaboration, there may be hybrid options that allow organizations to build evaluation capacity.Practical implicationsOrganisations are faced with a trade-off between commissioning an evaluation, and building internal evaluation capacity. To better understand each approach, it is important to consider the purpose and context of the evaluation. This shifts some commonly held assumptions about internal and external evaluations. Re-examining these assumptions will help organizations make a more informed decision about an evaluation approach.Originality/valueThe field of evaluation is particularly concerned with evaluation use. Most of the literature on this has focused on the approach of individual evaluators, and insufficient attention has been paid to the institutional architecture of the evaluation. This article considers how some of the organisational structures around an evaluation contribute to evidence use, and the case study of South Africa also shifts the focus to the central but overlooked role of context in the debate.     

Difference-based Methods for Truncating the Singular Value Decomposition

Given a noisy time series (or signal), one may wish to remove the noise from the observed series. Assuming that the noise-free series lies in some low-dimensional subspace of rank r, a common approach is to embed the noisy time series into a Hankel trajectory matrix. The singular value decomposition is then used to deconstruct the Hankel matrix into a sum of rank-one components. We wish to demonstrate that there may be some potential in using difference-based methods of the observed series in order to provide guidance regarding the separation of the noise from the signal, and to estimate the rank of the low-dimensional subspace in which the true signal is assumed to lie.     

Parameters estimation for weibull distributed lifetimes under progressive censoring with random removeals

This paper considers the estimation problem when lifetimes are Weibull distributed and are collected under a Type-II progressive censoring with random removals, where the number of units removed at each failure time follows a uniform discrete distribution. The expected time of this censoring plan is discussed and compared numerically to that under a Type II censoring without removal. Maximum likelihood estimator of the parameters and their asymptotic variances are derived.