基于模糊多属性群决策的维护方式选择研究

维护方式选择是维护管理中的重要工作之一,合理的维护方式既能达到保障设备的稳定运行,又能同时兼顾其它各个方面的要求。由于对维护方式的评价涉及多个部门、人员和属性,有些指标只是一个模糊的概念,因而采用模糊多属性群决策的方法对维护方式进行优先抉择。本文结合A公司的设备维护方式选择问题,尝试使用模糊多属性群决策折衷算法求解最佳的维护方式。     

基于经济新常态的长春市水资源开发利用潜力评价

以长春市为研究对象,基于未来经济处于新常态的发展态势,构建包含水资源、社会、经济、生态环境4个子系统的评价指标体系,并确定各指标的评价等级标准;基于模糊数学理论以及层次分析法构建包括目标层、准则层、指标层的3层模糊综合评价模型,以2015年为现状水平年,应用模型对长春市水资源开发利用潜力进行综合评价。结果表明:长春市水资源开发利用程度处于中等偏上水平,各辖区水资源开发利用规模不平衡,可通过产业结构调整提高全市用水效率,为经济持续增长提供保障。     

A Multicriteria Approach for Modeling Small Enterprise Credit Rating: Evidence from China

ABSTRACT

As the engine of China’s economy, small enterprises have been the central to the country’s economic development. However, given the characteristics of the small enterprises loan (i.e., short borrowing period, large volume, small amount and incomplete information), it is extremely challenging for financial institutions to assess their creditworthiness. Thus, it seriously delays and restricts the financing access for small enterprises. In an attempt to relieve the financing difficulty of small enterprises, this article makes use of 687 small wholesale and retail enterprises in a regional commercial bank in China, to establish a credit rating indicator system composed of 17 indicators by using both partial correlation analysis and probit regression. It then utilizes TOPSIS together with fuzzy C-means to score the credit ratings of our sample of small enterprises. With the dual test of default discrimination and ROC curve, the prediction accuracy of the established indicator system has reached 80.10% and 0.917, respectively, indicating the robustness and validity of our credit rating system.     

Simplified Jackknife Variance Estimates for Fuzzy Measures of Multidimensional Poverty

In this paper, we present a practical methodology for variance estimation for multi‐dimensional measures of poverty and deprivation of households and individuals, derived from sample surveys with complex designs and fairly large sample sizes. The measures considered are based on fuzzy representation of individuals' propensity to deprivation in monetary and diverse non‐monetary dimensions. We believe this to be the first original contribution for estimating standard errors for such fuzzy poverty measures. The second objective is to describe and numerically illustrate computational procedures and difficulties in producing reliable and robust estimates of sampling error for such complex statistics. We attempt to identify some of these problems and provide solutions in the context of actual situations. A detailed application based on European Union Statistics on Income and Living Conditions data for 19 NUTS2 regions in Spain is provided.     

Risk assessment for public–private partnership projects: using a fuzzy analytic hierarchical process method and expert opinion in China

Public–private partnerships (PPP) have been widely used in China to procure public facilities and services. Complicated problems in PPP projects in China arise because of a variety of risk factors. A proper risk assessment model is needed to identify risks and provide risk response strategies for future Chinese PPP projects. The fuzzy analytic hierarchy process (F-AHP) method provides the ability to solve complex risk assessment of PPP projects. Current risk assessment models are limited to PPP projects in specific countries and do not consider unique risks in China, especially political, economic, social, and legal risks. This paper designed a risk evaluation index system for PPP projects based on the Delphi expert investigation method, and then established an optimized risk evaluation model for PPP projects in China using the F-AHP method. The risks identified are confirmed by interviewing experts from Chinese local government departments, private enterprise, third-party intermediary consulting and regulatory agencies, and academic organizations. The results show that the risks that ranked among the top ten are closely related to China’s political and economic policies and relationships among stakeholders. It can be concluded that government authorities play a critical role in providing a favorable political, social, and economic environment and an effective institutional framework for PPP projects. Furthermore, it is also important to deal with relationships among stakeholders based on the public–private ‘win–win’ principle. This study provides risk response strategies, addressing key issues from eight aspects: an impeccable legal and regulatory framework; a central coordinating and regulating PPP authority; supportive governmental authorities; institutional capacity-building; favorable economic conditions and viability; community, partner, and moral accountability; clear division of responsibilities through contracts; and effective advisory management. These effective measures may be useful in reducing the adverse effects of risk for PPP projects in China.     

The impact of CSR on corporate reputation perceptions of the public—A configurational multi‐time,multi‐source perspective

This study investigates the connection between corporate social responsibility (CSR) and corporate reputation among the public using fuzzy set qualitative comparative analysis (fsQCA). To examine complex processes underlying the reactions of this influential stakeholder group, hypotheses are drawn from the category diagnosticity approach. Thereby, a psychological model of perceived (im)morality is transferred to the CSR context. In line with these hypotheses, positive/negative CSR activities influence reputation in the expected directions (H1a, b), while the effects of specific configurations of CSR activities reveal an asymmetry suggesting a negativity bias (H2). Further analyses confirm that positive effects require a consistent positive performance regarding prior reputations (H3a) and the aggregated CSR activities of several previous years (H3b, c). Moreover, the relevant patterns vary between industries (H4). The present study thus contributes to CSR research by investigating a powerful but hitherto understudied stakeholder group through a category diagnosticity lens combined with a configurational approach to analysis.     

The cointegrated vector autoregressive model with general deterministic terms

In the cointegrated vector autoregression (CVAR) literature, deterministic terms have until now been analyzed on a case-by-case, or as-needed basis. We give a comprehensive unified treatment of deterministic terms in the additive model $X_t=\gamma Z_t+Y_t$, where $Z_t$ belongs to a large class of deterministic regressors and $Y_t$ is a zero-mean CVAR. We suggest an extended model that can be estimated by reduced rank regression, and give a condition for when the additive and extended models are asymptotically equivalent, as well as an algorithm for deriving the additive model parameters from the extended model parameters. We derive asymptotic properties of the maximum likelihood estimators and discuss tests for rank and tests on the deterministic terms. In particular, we give conditions under which the estimators are asymptotically (mixed) Gaussian, such that associated tests are ${\chi }^2$-distributed.     

CAUSAL INFERENCE ON EDUCATION POLICIES: A SURVEY OF EMPIRICAL STUDIES USING PISA,TIMSS AND PIRLS

The identification of the causal effects of educational policies is the top priority in recent education economics literature. As a result, a shift can be observed in the strategies of empirical studies. They have moved from the use of standard multivariate statistical methods, which identify correlations or associations between variables only, to more complex econometric strategies, which can help to identify causal relationships. However, exogenous variations in databases have to be identified in order to apply causal inference techniques. This is a far from straightforward task. For this reason, this paper provides an extensive and comprehensive overview of the literature using quasi‐experimental techniques applied to three well‐known international large‐scale comparative assessments, such as PISA, PIRLS or TIMSS, over the period 2004–2016. In particular, we review empirical studies employing instrumental variables, regression discontinuity designs, difference in differences and propensity score matching to the above databases. Additionally, we provide a detailed summary of estimation strategies, issues treated and profitability in terms of the quality of publications to encourage further potential evaluations. The paper concludes with some operational recommendations for prospective researchers in the field.     

The endo–exo problem in high frequency financial price fluctuations and rejecting criticality

The endo–exo problem lies at the heart of statistical identification in many fields of science, and is often plagued by spurious strong-and-long memory due to improper treatment of trends, shocks and shifts in the data. A class of models that has shown to be useful in discerning exogenous and endogenous activity is the Hawkes process. This class of point processes has enjoyed great recent popularity and rapid development within the quantitative finance literature, with particular focus on the study of market microstructure and high frequency price fluctuations. We show that there are important lessons from older fields like time series and econometrics that should also be applied in financial point process modelling. In particular, we emphasize the importance of appropriately treating trends and shocks for the identification of the strength and length of memory in the system. We exploit the powerful Expectation Maximization algorithm and objective statistical criteria (BIC) to select the flexibility of the deterministic background intensity. With these methods, we strongly reject the hypothesis that the considered financial markets are critical at univariate and bivariate microstructural levels.     

TOOLS FOR RE‐RANDOMIZATION

With the rising popularity of field experiments in economics, re‐randomization schemes have emerged as tools to induce balance in observable variables across treatment groups. However, re‐randomization is not fully understood and the methodologies to estimate its effects on the distribution of parameters are still under‐developed. This paper helps to close that gap by suggesting an asymptotically normal re‐randomization scheme and bootstrapping procedure to carry inference under a wide range of estimators and re‐randomization schemes.