Estimation of Sparse Structural Parameters with Many Endogenous Variables

We apply the generalized method of moments–least absolute shinkage and selection operator (GMM-Lasso) (Caner, 2009) to a linear structural model with many endogenous regressors. If the true parameter is sufficiently sparse, we can establish a new oracle inequality, which implies that GMM-Lasso performs almost as well as if we knew a priori the identities of the relevant variables. Sparsity, meaning that most of the true coefficients are too small to matter, naturally arises in econometric applications where the model can be derived from economic theory. In addition, we propose to use a modified version of AIC or BIC to select the tuning parameter in practical implementation. Simulations provide supportive evidence concerning the finite sample properties of the GMM-Lasso.     

Approximating cross-validatory predictive evaluation in Bayesian latent variable models with integrated IS and WAIC

Looking at predictive accuracy is a traditional method for comparing models. A natural method for approximating out-of-sample predictive accuracy is leave-one-out cross-validation (LOOCV)—we alternately hold out each case from a full dataset and then train a Bayesian model using Markov chain Monte Carlo without the held-out case; at last we evaluate the posterior predictive distribution of all cases with their actual observations. However, actual LOOCV is time-consuming. This paper introduces two methods, namely iIS and iWAIC, for approximating LOOCV with only Markov chain samples simulated from a posterior based on a full dataset. iIS and iWAIC aim at improving the approximations given by importance sampling (IS) and WAIC in Bayesian models with possibly correlated latent variables. In iIS and iWAIC, we first integrate the predictive density over the distribution of the latent variables associated with the held-out without reference to its observation, then apply IS and WAIC approximations to the integrated predictive density. We compare iIS and iWAIC with other approximation methods in three kinds of models: finite mixture models, models with correlated spatial effects, and a random effect logistic regression model. Our empirical results show that iIS and iWAIC give substantially better approximates than non-integrated IS and WAIC and other methods.     

Diagnostics on nonlinear model with scale mixtures of skew-normal and first-order autoregressive errors

In this work, we develop some diagnostics for nonlinear regression model with scale mixtures of skew-normal (SMSN) and first-order autoregressive errors. The SMSN distribution class covers symmetric as well as asymmetric and heavy-tailed distributions, which offers a more flexible framework for modelling. Maximum-likelihood (ML) estimates are computed via an expectation–maximization-type algorithm. Local influence diagnostics and score test for the correlation are also derived. The performances of the ML estimates and the test statistic are investigated through Monte Carlo simulations. Finally, a real data set is used to illustrate our diagnostic methods.     

长三角城市群产业联动协同转型的机制与对策

本文主要研究了长三角区域一体化发展过程中城市群内部中心城市与各县市产业联动的现实基础、发展现状及趋势,以及目前存在的主要问题及障碍等,并提出如何通过中心城市与各市县产业联动来实现长三角地区产业转型升级的机制及政策.     

Impacts of Power Structure on Supply Chains with Uncertain Demand

In this study, we use a game‐theory‐based framework to model power in a supply chain with random and price‐dependent demand and examine how power structure and demand models (expected demand and demand shock) affect supply chain members' performance. We demonstrate that whether a firm benefits from its power depends on the expected demand model but not on demand shock model. A firm benefits from its power only for linear but not for constant elasticity expected demand. The impact of power structure on supply chain efficiency depends on the models of both expected demand and demand shock. With additive shock, supply chain efficiency is highest (lowest) when neither firm dominates for linear (constant elasticity) expected demand. With multiplicative shock, the supply chain efficiency is highest with a power retailer (manufacturer) for linear (constant elasticity) expected demand. The manufacturer always benefits from a reduction in demand uncertainty. However, the retailer loses (benefits) from demand uncertainty reduction for linear (constant elasticity) expected demand. With a power retailer, the retail price is always on the higher end for linear expected demand, and the customer service level is the lowest for constant elasticity expected demand. Consequently, consumers do not necessarily benefit from a power retailer.     

基于组织特异性免疫视角的质量绩效提升路径研究——投影寻踪法和强迫进入法的实证分析

本文以组织特异性免疫为切入点,基于组织质量监视、组织质量防御和组织质量记忆视角,选取前因变量、中介变量和调节变量构建质量绩效提升路径的理论分析框架,采用投影寻踪法和强迫进入法对已构建的路径框架进行实证分析,得出提升质量绩效的路径,研究结论将为企业提升质量绩效提供理论依据和实践指导。     

论新经济政策与十月革命的辩证关系

落后国家的无产阶级率先夺取政权并不违背历史辩证法;但是,由于过渡的起点发生了改变,因此,落后国家就不能简单照搬马克思的直接过渡理论,而必须寻找一条新的过渡道路。新经济政策是从资本主义向社会主义迂回过渡的政策,即利用资本主义的政策,实质上是承认落后国家不具备向社会主义直接过渡的条件;但新经济政策不是对十月革命的否定。“两个必然”与“两个决不会”、率先夺取政权与迂回过渡实际上是辩证统一的关系,统一的基础是“生产力决定生产关系,生产关系一定要适合生产力的性质”这一唯物史观的基本原理。     

藏民族传统体育研究的理论思考

通过对藏民族传统体育的社会价值和学术价值的探讨,揭示藏民族传统体育是民族文化的重要组成部分,阐述藏民族传统体育对丰富民族文化,增强民族体质,促进藏民族地区社会稳定和经济发展具有重要作用,并从理论上提出了研究藏民族传统体育的基本思路。     

关于企业规模与企业绩效关系的实证研究

以我国深沪上市公司为样本,参照国家企业规模划分标准,将企业分为主板大企业,中小板与创业板中小企业,实证检验了企业规模与绩效之间的关系。研究表明,影响绩效的因素是多方面的,规模并不是决定绩效的唯一因素;虽然不同行业的企业规模与企业绩效均未呈现显著的强正相关关系,但规模各指标对不同类型企业的绩效依然存在着不同的影响;规模效率低是造成我国企业规模与绩效不显著相关的主要原因之一。     

京津冀医疗卫生资源配置的公平性

分析了京津冀医疗资源配置的现状,并利用基尼系数、泰尔指数测算了其公平性程度和变动趋势。结果显示：京津冀按人口配置的医疗资源公平性呈逐年递增的趋势,卫生技术人员、医师、护士的区域间差异大于区域内的差异,卫生机构床位的区域内差异大于区域间差异,京津冀医疗机构床位配置的公平性要大于医疗人力配置的公平性。京津冀医疗资源配置的差异,主要表现为优质医疗资源的差异。