异质产品供应链定价控制权与零售商横向兼并效应分析

针对一部线性合约与供应商是否具有绝对定价控制权,比较分析零售商横向兼并集中度提高对消费者福利的影响.结果表明,(1)当供应商具有绝对定价控制权时,零售商横向兼并对消费者产生的影响取决于产品替代性程度大小:若替代性程度较高,则将产生有利影响,反之,将产生不利影响;(2)当供应商没有绝对定价控制权时,零售商横向兼并对消费者产生的影响取决于零售商兼并前的相对定价控制权强弱与兼并后的相对定价控制权增幅大小:若零售商在供应链中处于相对弱势地位,且零售商横向兼并使零售商的定价控制权增幅较大,则兼并将对消费者产生有利影响,否则,将对消费者产生不利影响.     

不同模型在财务预警实证中的比较研究

研究当前国内外公司财务困境预测问题的现状,寻找一套适合于我国企业的财务状况识别指标体系;然后依据该指标体系采用不同方法建立财务状况预测模型;最后,利用样本公司实际指标数据对各个模型的短期及中期预测效果进行了比较分析与实证研究。     

交易额、A 股比例、势效应和三因子模型

利用回归和构造动态投资组合方法,对中国股票市场1995 年7 月到2000 年12 月股票 月收益率、交易额、总市值及其财务数据进行分析,发现中国股票市场具有显著的交易额效应、 A 股比例效应、市值效应、账面市值比效应等. 这些效应有密切的相互关系,但不能用市场beta 值来解释. 市场beta 值、市值因子、账面市值比因子一起,也就是Fama2French 三因子模型,可以 很好地解释这些效应     

Pipage Rounding: A New Method of Constructing Algorithms with Proven Performance Guarantee

The paper presents a general method of designing constant-factor approximation algorithms for some discrete optimization problems with assignment-type constraints. The core of the method is a simple deterministic procedure of rounding of linear relaxations (referred to as pipage rounding). With the help of the method we design approximation algorithms with better performance guarantees for some well-known problems including MAXIMUM COVERAGE, MAX CUT with given sizes of parts and some of their generalizations.     

资本下乡：进入模式选择及其影响因素

进入模式是经济组织配置资源的重要手段。以工商资本对农户经营活动的控制权程度为依据,将工商资本投资农业的进入模式划分为“松散型”、“半紧密型”、“紧密型”三种类型。利用对广东、湖北、江苏以及云南四省151个投资主体的调查数据,采用有序逻辑回归模型分析生产因素、市场因素、制度因素以及生产技术传递能力对工商资本投资农业的进入模式选择的影响。结果表明：耕地资源优势越弱,工商资本投资农业选择紧密型进入模式的可能性越高;市场潜力越大,越倾向于选择紧密型进入模式;非正式制度越不利,选择紧密型进入模式的可能性越低;生产技术的传递能力越弱,越倾向于选择紧密型进入模式。     

农村老年人日常生活家庭照料与社会照料关系研究——基于多层回归模型的分析

文章利用中国健康与养老追踪调查 2011—2012 年的基线调查数据,运用多层回归模型,通过分析社会照料对农村老年人家庭照料获得的影响,探讨家庭照料与社会照料的关系。 回归估计结果显示,个人层次上的年龄、居住状况、失能情况和社区层次上的老龄补贴、经济状况对农村老年人获得家庭照料有不同程度的影响,而代表社会照料的变量在统计上均不显著。 回归估计结果说明,社会照料并不能对缺位的家庭照料起到补充作用,更不会起到替代作用,两者呈现一种非此即彼的“无关系”。 文章的政策启示是,打破家庭照料与社会照料的壁垒,构建一个“流动”的老年照料体系。     

Testing Inference in Inflated Beta Regressions under Model Misspecification

We consider testing inference in inflated beta regressions subject to model misspecification. In particular, quasi-z tests based on sandwich covariance matrix estimators are described and their finite sample behavior is investigated via Monte Carlo simulations. The numerical evidence shows that quasi-z testing inference can be considerably more accurate than inference made through the usual z tests, especially when there is model misspecification. Interval estimation is also considered. We also present an empirical application that uses real (not simulated) data.     

Estimation for Partially Linear Single-index Instrumental Variables Models

In this article, we generalize the partially linear single-index models to the scenario with some endogenous covariates variables. It is well known that the estimators based on the existing methods are often inconsistent because of the endogeneity of covariates. To deal with the endogenous variables, we introduce some auxiliary instrumental variables. A three-stage estimation procedure is proposed for partially linear single-index instrumental variables models. The first stage is to obtain a linear projection of endogenous variables on a set of instrumental variables, the second stage is to estimate the link function by using local linear smoother for given constant parameters, and the last stage is to obtain the estimators of constant parameters based on the estimating equation. Asymptotic normality is established for the proposed estimators. Some simulation studies are undertaken to assess the finite sample performance of the proposed estimation procedure.     

On Combining Wavelets Expansion and Sparse Linear Models for Regression on Metabolomic Data and Biomarker Selection

Wavelet thresholding of spectra has to be handled with care when the spectra are the predictors of a regression problem. Indeed, a blind thresholding of the signal followed by a regression method often leads to deteriorated predictions. The scope of this article is to show that sparse regression methods, applied in the wavelet domain, perform an automatic thresholding: the most relevant wavelet coefficients are selected to optimize the prediction of a given target of interest. This approach can be seen as a joint thresholding designed for a predictive purpose. The method is illustrated on a real world problem where metabolomic data are linked to poison ingestion. This example proves the usefulness of wavelet expansion and the good behavior of sparse and regularized methods. A comparison study is performed between the two-steps approach (wavelet thresholding and regression) and the one-step approach (selection of wavelet coefficients with a sparse regression). The comparison includes two types of wavelet bases, various thresholding methods, and various regression methods and is evaluated by calculating prediction performances. Information about the location of the most important features on the spectra was also obtained and used to identify the most relevant metabolites involved in the mice poisoning.     

A new approach to statistical efficiency of weighted least squares fitting algorithms for reparameterization of nonlinear regression models

We study nonlinear least-squares problem that can be transformed to linear problem by change of variables. We derive a general formula for the statistically optimal weights and prove that the resulting linear regression gives an optimal estimate (which satisfies an analogue of the Rao-Cramer lower bound) in the limit of small noise.