Diagnostics of multiple group influential observations for logistic regression models

In this paper, two new multiple influential observation detection methods, GCD.GSPR and mCD*, are introduced for logistic regression. The proposed diagnostic measures are compared with the generalized difference in fits (GDFFITS) and the generalized squared difference in beta (GSDFBETA), which are multiple influential diagnostics. The simulation study is conducted with one, two and five independent variable logistic regression models. The performance of the diagnostic measures is examined for a single contaminated independent variable for each model and in the case where all the independent variables are contaminated with certain contamination rates and intensity. In addition, the performance of the diagnostic measures is compared in terms of the correct identification rate and swamping rate via a frequently referred to data set in the literature.     

The Eurasian Economic Union: Development Prospects and Possible Obstacles

This article analyzes the development prospects and problems for the Eurasian Economic Union (EAEU). It examines issues of integration within the EAEU, as well as interactions with other countries, both CIS (Commonwealth of Independent States) and non-CIS. The main problem of integration within the EAEU is the dominance of redistributive motives over productive ones. This article assesses the extent of Russian oil and gas transfers to EAEU partners and the impact of tax maneuvers on their size. It shows that the creation of mechanisms for redistributing profits within the EAEU will allow a positive economic effect to materialize from free trade agreements with non-CIS countries. The article assesses the risks for the EAEU related to Russia’s introduction of a unilateral ban on food imports from countries on the sanctions list and to the possible establishment of tariffs on trade with Ukraine.     

An additive–multiplicative mean model for panel count data with dependent observation and dropout processes

This paper discusses regression analysis of panel count data with dependent observation and dropout processes. For the problem, a general mean model is presented that can allow both additive and multiplicative effects of covariates on the underlying point process. In addition, the proportional rates model and the accelerated failure time model are employed to describe possible covariate effects on the observation process and the dropout or follow‐up process, respectively. For estimation of regression parameters, some estimating equation‐based procedures are developed and the asymptotic properties of the proposed estimators are established. In addition, a resampling approach is proposed for estimating a covariance matrix of the proposed estimator and a model checking procedure is also provided. Results from an extensive simulation study indicate that the proposed methodology works well for practical situations, and it is applied to a motivating set of real data.     

Fast and approximate exhaustive variable selection for generalised linear models with APES

We present APproximated Exhaustive Search (APES), which enables fast and approximated exhaustive variable selection in Generalised Linear Models (GLMs). While exhaustive variable selection remains as the gold standard in many model selection contexts, traditional exhaustive variable selection suffers from computational feasibility issues. More precisely, there is often a high cost associated with computing maximum likelihood estimates (MLE) for all subsets of GLMs. Efficient algorithms for exhaustive searches exist for linear models, most notably the leaps‐and‐bound algorithm and, more recently, the mixed integer optimisation (MIO) algorithm. The APES method learns from observational weights in a generalised linear regression super‐model and reformulates the GLM problem as a linear regression problem. In this way, APES can approximate a true exhaustive search in the original GLM space. Where exhaustive variable selection is not computationally feasible, we propose a best‐subset search, which also closely approximates a true exhaustive search. APES is made available in both as a standalone R package as well as part of the already existing mplot package.     

A Graphical Tool for Interpreting Regression Coefficients of Trinomial Logit Models

Multinomial logit (also termed multi-logit) models permit the analysis of the statistical relation between a categorical response variable and a set of explicative variables (called covariates or regressors). Although multinomial logit is widely used in both the social and economic sciences, the interpretation of regression coefficients may be tricky, as the effect of covariates on the probability distribution of the response variable is nonconstant and difficult to quantify. The ternary plots illustrated in this article aim at facilitating the interpretation of regression coefficients and permit the effect of covariates (either singularly or jointly considered) on the probability distribution of the dependent variable to be quantified. Ternary plots can be drawn both for ordered and for unordered categorical dependent variables, when the number of possible outcomes equals three (trinomial response variable); these plots allow not only to represent the covariate effects over the whole parameter space of the dependent variable but also to compare the covariate effects of any given individual profile. The method is illustrated and discussed through analysis of a dataset concerning the transition of master’s graduates of the University of Trento (Italy) from university to employment.     

浙江省城市绿色技术创新效率评价及影响因素分析

基于网络DEA模型和Malmquist指数模型，利用浙江省11个地级市的2011-2020年的数据，以降维后的污染指数为非期望产出对绿色技术创新效率进行测度和分析。发现:浙江各市总体上成果转化阶段的效率优于研发阶段，城市间发展差异较大；整体TFP指数均值为1.121，其提升主要源自技术进步；城镇化水平、产业结构优化、对外开放、政府干预和研发强度对绿色技术创新效率有促进作用，但经济发展水平与绿色技术创新效率的U型关系不显著。     

影子银行、地方政府债务及金融发展动态关系研究——基于省级面板VAR模型

基于2004—2017年中国省级面板数据,运用面板向量自回归(PVAR)模型,使用系统GMM估计、脉冲响应函数、方差分解以及格兰杰因果关系检验等方法分析了影子银行、地方政府债务及金融发展之间的动态关系.结果表明:影子银行、地方政府债务与金融发展水平三者之间存在动态耦合关系.在地方政府融资能力受到约束的情况下,影子银行为地方政府提供了多元的融资方式,在增加政府融资能力的同时提升了政府债务水平;而地方政府债务需求显著推动了影子银行规模的快速发展.同时,影子银行过度扩张危害了金融市场的健康发展,降低金融发展水平,继而使地方政府的融资渠道受到约束.但金融发展并不能有效约束影子银行规模,原因在于,政府融资需求是影子银行的主要动力,若不能控制地方政府的借贷行为则无法从源头解决问题.监管机构在去杠杆的过程中,应该综合考虑影子银行与地方政府债务、金融发展之间的动态关系,如此才能够实现预期的政策效果.     

医疗信息共享对患者转移数量和服务质量水平影响研究

当前我国医院间存在严重的"信息孤岛"，医院参与医疗信息共享意愿不高，患者的诊疗信息被静态碎片化储存而无法充分有效地利用。考虑到医院进行医疗信息共享将降低患者转移成本，本文构建一个多阶段双寡头动态博弈模型研究医疗信息共享对医院竞争过程中患者转移数量和服务质量水平决策的影响。首先，根据是否存在转移成本，将患者分为新患者和经验性患者，借助Hotelling模型刻画患者的效用函数，分析患者就诊决策。然后，在政府价格规制和不考虑医院利他性情景下，构建了医院累积期望收益目标函数，使用动态规划方法，求解实现医院累积期望收益最大化的服务质量水平，获得了实现患者相互转移且医院在市场中共存的马尔可夫完美均衡。最后，根据医院参与医疗信息共享后患者转移成本降为零，分析与比较信息共享前后患者转移数量和服务质量水平变化。研究发现：在不同医院间本身存在患者转移背景下，医院参与信息共享后，患者转移数量增加但存在一个上限，增加的转移量与患者在医院间的转移成本呈正相关，与初始感知效用的差值范围呈负相关；医院参与信息共享后，均衡状态下的医院服务质量水平高于信息共享前的服务质量水平。因此，在不改变当前医保支付方式下，要加快推进医疗信息共享，政府部门可以根据医院的患者数量和服务质量水平变化对其进行定期补贴，以激励医院积极参与医疗信息共享，本文给出了这个补贴的量化表达。     

中介机构参与下联盟企业知识转移的三方利益博弈分析

知识转移是企业保存知识、提升创新能力的重要途径，中介机构在转移过程中发挥了重要作用。本文以联盟企业间知识转移为研究对象，考虑了企业的不同心理压力，首先建立了联盟企业双方的讨价还价博弈模型；其次构建了考虑中介机构参与知识转移的博弈模型，分析和讨论三方收益变化的影响因素及策略选择。结果表明，在直接知识转移博弈中，转移主体的收益大小与自身的心理压力呈负相关，而与对方的心理压力呈正相关，同等心理压力下知识转出方占优；中介机构参与博弈情形下，中介机构作为协作角色的博弈收益仅受博弈双方心理压力影响，且与知识接收方协作可获得更大收益，而主导角色博弈情形下收益受到三方主体心理压力的综合影响，并在主导知识接收博弈时获得收益更大；中介机构的角色选择与知识转出方心理压力变化相关性不明显，主要受到知识接收方及自身心理压力影响。