How does distance affect market entry mode choice? Evidence from French companies

The objective of this article is to examine the relevant dimensions of distance for foreign market entry mode choice. Based on a sample of 203 interfirm linkages formed by French multinationals with partners across the world, we analyze the impact of four dimensions of distance on the choice between cooperative alliances and mergers-acquisitions. The findings indicate that administrative and economic distance have a significant influence on market entry mode choice, whereas the impact of cultural and geographic distance is not significant. They further highlight the important role of the host country's government effectiveness for market entry mode decisions.     

Bayesian modelling of catch in a north-west Atlantic fishery

Summary. We model daily catches of fishing boats in the Grand Bank fishing grounds. We use data on catches per species for a number of vessels collected by the European Union in the context of the Northwest Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used and the mesh size of the nets) are obvious candidates, but one can also consider the season or the actual location of the catch. Our database leads to 28 possible regressors (arising from six continuous variables and four categorical variables, whose 22 levels are treated separately), resulting in a set of 177 million possible linear regression models for the log-catch. Zero observations are modelled separately through a probit model. Inference is based on Bayesian model averaging, using a Markov chain Monte Carlo approach. Particular attention is paid to the prediction of catches for single and aggregated ships.     

Estimation of generalized linear latent variable models

Summary.  Generalized linear latent variable models (GLLVMs), as defined by Bartholomew and Knott, enable modelling of relationships between manifest and latent variables. They extend structural equation modelling techniques, which are powerful tools in the social sciences. However, because of the complexity of the log-likelihood function of a GLLVM, an approximation such as numerical integration must be used for inference. This can limit drastically the number of variables in the model and can lead to biased estimators. We propose a new estimator for the parameters of a GLLVM, based on a Laplace approximation to the likelihood function and which can be computed even for models with a large number of variables. The new estimator can be viewed as an M -estimator, leading to readily available asymptotic properties and correct inference. A simulation study shows its excellent finite sample properties, in particular when compared with a well-established approach such as LISREL. A real data example on the measurement of wealth for the computation of multidimensional inequality is analysed to highlight the importance of the methodology.     

A Semi-parametric Regression Model with Errors in Variables

Abstract.  In this paper, we consider a partial linear regression model with measurement errors in possibly all the variables. We use a method of moments and deconvolution to construct a new class of parametric estimators together with a non-parametric kernel estimator. Strong convergence, optimal rate of weak convergence and asymptotic normality of the estimators are investigated.     

多仁·丹增班觉对西藏传统音乐的影响

18世纪末 ,西藏地方政府噶伦多仁·丹增班觉从北京带回了扬琴、笙、横笛等一些内地乐器 ,并且传授演奏技法 ,组建乐队 ,配以小调演唱 ,出现了器乐合奏的艺术形式。多仁·丹增班觉对西藏传统音乐的发展 ,特别是高雅、优美的囊玛音乐的形成做出了贡献。     

论化工类研究生《仪器分析》课程教学体系的建设

分析了化学工艺类研究生《仪器分析》课程教学现状和教学实施过程中存在的问题 ,针对教学内容、教学方式及教学考核提出了相应的解决方案和建议。     

Joint impact of multiple observations on a subset of variables in multiple linear regression

In multiple linear regression analysis, each observation affects the fitted regression equation differently and has varying influences on the regression coefficients of the different variables. Chatterjee & Hadi (1988) have proposed some measures such as DSSE_ij (Impact on Residual Sum of Squares of simultaneously omitting the ith observation and the jth variable), F_j (Partial F-test for the jth variable) and F_j(i) (Partial F-test for the jth variable omitting the ith observation) to show the joint impact and the interrelationship that exists among a variable and an observation. In this paper we have proposed more extended form of those measures DSSE_IJ, F_J and F_J(I) to deal with the interrelationships that exist among the multiple observations and a subset of variables by monitoring the effects of the simultaneous omission of multiple variables and multiple observations.     

国家治理中的第三次分配:美国镜鉴与中国道路

第三次分配在中国推动共同富裕迈入新阶段的当下扮演着重要角色。基于国家社会关系视角,将第三次分配划分为强政府大社会、弱政府大社会、强政府小社会、弱政府小社会4种实践模式。美国第三次分配建构起了弱政府大社会实践模式,以社会主导、政府监管、政治中立为主要特征。工具理性是美国第三次分配的成功经验,形成了文化、组织、制度及技术“四位一体”的逻辑理路。隐藏在工具理性外衣下的阶级理性是美国第三次分配的失败教训,在阶级理性思维影响下,社会“私”的方面被放大,形成了以捐赠者为中心的家长制作风。中国第三次分配是先富带动后富、助推共同富裕的实现方式,它深嵌到中国式现代化进程中。中国第三次分配不能照搬美国模式,需要在充分吸收工具理性和摒弃阶级理性的经验教训基础上,塑造强政府大社会模式,将中国文化要素融入制度体系,增强第三次分配主体规范性,加大第三次分配激励力度,完善第三次分配监管体系,持续并高度关注贫困人群。     

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.     

Limit theorems for dependent Bernoulli variables with statistical inference

We consider a class of dependent Bernoulli variables where the conditional success probability is a linear combination of the last few trials and the original success probability. We obtain its limit theorems including the strong law of large numbers, weak invariance principle, and law of the iterated logarithm. We also derive some statistical inference results which make the model applicable. Simulation results are exhibited as well to show that with small sample size the convergence rate is satisfying and the proposed estimators behave well.