采油厂流量仪表选型探讨

流量计的选型直接影响到流量计量的准确度。根据被测介质性质的不同、流量大小和具体的现场条件选择合适的计量仪表,既降低了计量误差,又做到经济耐用,满足了现场计量需要。     

基于交易成本理论的工程合同类型与交易成本探讨

工程合同类型选择问题在工程实践中研究较为广泛,然而对于该问题的更深层次的探讨,特别是在经济学领域的探索却相对较少。在经济学理论影响社会显著发展的当今,应用经济学理论探讨社会经济问题是十分必要的。论文以新制度经济学中交易成本理论研究成果为基础,通过对交易成本的重新理解,分析其与不同合同类型问的联系,得到具有一定指导性的结论,有助于进一步研究和探索。     

Evaluating asset pricing models using the second Hansen-Jagannathan distance

We develop a specification test and a sequence of model selection procedures for non-nested, overlapping, and nested models based on the second Hansen-Jagannathan distance, which requires a good asset pricing model to not only have small pricing errors but also be arbitrage free. Our methods have reasonably good finite sample performances and are more powerful than existing ones in detecting misspecified models with small pricing errors but are not arbitrage-free and in differentiating models that have similar pricing errors of a given set of test assets. Using the Fama and French size and book-to-market portfolios, we reach dramatically different conclusions on model performances based on our approach and existing methods.     

项目组合选择研究：基于模糊理论的模型建构及验证

项目组合的选定与企业经营息息相关,然而传统的选择方法存在许多限制。本文应用模糊理论建构一个项目组合的决策模型,模型中纳入了成本、利润及风险等3个决策因子,探讨在资源有限的情况下项目成本及获利的交互影响关系,并利用模糊归属函数建构一个数学规划模型。为了验证模型的有效性,用此模型与传统模型进行求解比较,证实了所提出模型的实用性。     

Adaptation and inertia in dynamic environments

Research summary : We address conflicting claims and mixed empirical findings about adaptation as a response to increased environmental dynamism. We disentangle distinct dimensions of environmental dynamism—the direction, magnitude, and frequency of change—and identify how selection shapes adaptive responses to these dimensions. Our results show how frequent directional changes undermine the value of exploration and decisively shift performance advantages to inert organizations that restrict exploration. In contrast, increased environmental variance rewards exploration. Our results also show that, in dynamic environments, the best‐performing organizations are generally more inert than less successful organizations. Managerial summary : Our research helps managers to understand under what business conditions investments into exploration and strategic flexibility are more likely to pay off. Dynamic business environments characterized by persistent trends and by large, infrequently occurring structural shocks reward strategic pursuit of temporary advantage. Thus, exploration and strategic flexibility are preferred strategies. In contrast, the challenge in frequently changing environments with fleeting opportunities is to identify and to focus on strategic actions whose payoffs on average are high, independent of environmental volatility. Low levels of exploration and long‐term strategic focus are preferred strategies in these circumstances. Copyright © 2015 John Wiley & Sons, Ltd.     

Recruitment and selection services: Efficiency and competitive reasons in the outsourcing of HR practices

While human resource (HR) activities have traditionally been performed internally, the outsourcing of HR practices is a rapidly increasing phenomenon. The accelerated rate of HR outsourcing also corresponds to a sweeping change in which non-transactional activities, such as recruitment, selection and training, are among the most outsourced HR practices. This article investigates the outsourcing decisions of recruiting and selection (R&S). It develops a predictive model based on efficiency drivers, rooted in transaction cost economy (TCE), and competitive motivations, derived from the resource-based view. The model has been tested in a sample of 276 medium and large enterprises in two specific contexts: the outsourcing of administrative R&S practices (job advertisement and pre-screening) and that of the more strategic R&S practices (colloquia and selection). Findings confirm the relevance of both categories of predictors, but they reveal how efficiency motivations are more important for the decisions to outsource administrative R&S practices while competitive issues matter more for the strategic side of R&S activity. Theoretical and managerial implications are offered on the basis of such evidence.     

Which covariates should be controlled in propensity score matching? Evidence from a simulation study

Propensity score matching is a widely‐used method to measure the effect of a treatment in social as well as medicine sciences. An important issue in propensity score matching is how to select conditioning variables in estimation of the propensity scores. It is commonly mentioned that variables which affect both program participation and outcomes are selected. Using Monte Carlo simulation, this paper shows that efficiency in estimation of the Average Treatment Effect on the Treated can be gained if all the available observed variables in the outcome equation are included in the estimation of propensity scores. This result still holds in the presence of non‐sampling errors in the observed control variables.     

Proportional Odds Models with High‐Dimensional Data Structure

The proportional odds model is the most widely used model when the response has ordered categories. In the case of high‐dimensional predictor structure, the common maximum likelihood approach typically fails when all predictors are included. A boosting technique pomBoost is proposed to fit the model by implicitly selecting the influential predictors. The approach distinguishes between metric and categorical predictors. In the case of categorical predictors, where each predictor relates to a set of parameters, the objective is to select simultaneously all the associated parameters. In addition, the approach distinguishes between nominal and ordinal predictors. In the case of ordinal predictors, the proposed technique uses the ordering of the ordinal predictors by penalizing the difference between the parameters of adjacent categories. The technique has also a provision to consider some mandatory predictors (if any) that must be part of the final sparse model. The performance of the proposed boosting algorithm is evaluated in a simulation study and applications with respect to mean squared error and prediction error. Hit rates and false alarm rates are used to judge the performance of pomBoost for selection of the relevant predictors.