固定效应部分线性变系数面板模型的快速有效估计

本文将最小二乘支持向量机(LSSVM) 和二次推断函数法(QIF) 相结合，为个体内具有相关结构的固定效应部分线性变系数面板模型提供了一种新的快速估计方法；在一定的正则条件下，论证了参数估计量的渐近正态性和非参数估计量的收敛速度；采用Monte Carlo模拟考察了估计方法在有限样本下的表现并将估计技术应用于现实数据分析。该方法不仅保证了估计的有效性和统计推断力，而且程序运行速度得到较大幅度提升。     

阿拉伯-伊斯兰世界群体文化心理蠡测(上)

曾经的辉煌与当下的衰败、曾经的核心与目前的边缘化所造成的失落感,三大圣地、《古兰经》与先知穆罕默德的相继蒙羞和遭辱所带来的受辱感,历史的创伤记忆与现实的生存困窘共同积淀成了一种顽强的集体潜意识,形成阿拉伯—伊斯兰民众仇恨与暴力相交织的心理死结,外现为以暴易暴、铤而走险甚至不断丧失自我。因此,阿拉伯—伊斯兰世界应在不断抵抗西方霸权主义对中东诸多利益的制衡中,努力变革滞后于全球化主潮的落后现状。否则,将面临更为严重的民族和地区的倒退,危及世界和平与稳定。     

Testing Hypotheses in Nonparametric Models of Production

Data envelopment analysis (DEA) and free disposal hull (FDH) estimators are widely used to estimate efficiency of production. Practitioners use DEA estimators far more frequently than FDH estimators, implicitly assuming that production sets are convex. Moreover, use of the constant returns to scale (CRS) version of the DEA estimator requires an assumption of CRS. Although bootstrap methods have been developed for making inference about the efficiencies of individual units, until now no methods exist for making consistent inference about differences in mean efficiency across groups of producers or for testing hypotheses about model structure such as returns to scale or convexity of the production set. We use central limit theorem results from our previous work to develop additional theoretical results permitting consistent tests of model structure and provide Monte Carlo evidence on the performance of the tests in terms of size and power. In addition, the variable returns to scale version of the DEA estimator is proved to attain the faster convergence rate of the CRS-DEA estimator under CRS. Using a sample of U.S. commercial banks, we test and reject convexity of the production set, calling into question results from numerous banking studies that have imposed convexity assumptions. Supplementary materials for this article are available online.     

阿拉伯语语法推理思维的文化探析

语法是语言的结构方式和组织规则,它兼具人类共性和民族个性.语法规则是人类长期抽象思维的成果和社团约定,它反映着语言社团的文化规约和语言学家的思维方式.阿拉伯语语法与汉语截然不同,与印欧语系语言相似,属于有明显形态标记的形式语法,具有清楚的逻辑推理思维,即遵循概念、判断和推理的基础模式.本文对此现象进行了阐释,并探究其文化成因.     

大数据背景下网络调查样本的建模推断问题研究——以广义Boosted模型的倾向得分推断为例

随着大数据和网络的不断发展,网络调查越来越广泛,大部分网络调查样本属于非概率样本,难以采用传统的抽样推断理论进行推断,如何解决网络调查样本的推断问题是大数据背景下网络调查发展的迫切需求。本文首次从建模的角度提出了解决该问题的基本思路：一是入样概率的建模推断,可以考虑构建基于机器学习与变量选择的倾向得分模型来估计入样概率推断总体;二是目标变量的建模推断,可以考虑直接对目标变量建立参数、非参数或半参数超总体模型进行估计;三是入样概率与目标变量的双重建模推断,可以考虑进行倾向得分模型与超总体模型的加权估计与混合推断。最后,以基于广义Boosted模型的入样概率建模推断为例演示了具体解决方法。     

Inferences from Cross-Sectional,Stochastic Frontier Models

Conventional approaches for inference about efficiency in parametric stochastic frontier (PSF) models are based on percentiles of the estimated distribution of the one-sided error term, conditional on the composite error. When used as prediction intervals, coverage is poor when the signal-to-noise ratio is low, but improves slowly as sample size increases. We show that prediction intervals estimated by bagging yield much better coverages than the conventional approach, even with low signal-to-noise ratios. We also present a bootstrap method that gives confidence interval estimates for (conditional) expectations of efficiency, and which have good coverage properties that improve with sample size. In addition, researchers who estimate PSF models typically reject models, samples, or both when residuals have skewness in the “wrong” direction, i.e., in a direction that would seem to indicate absence of inefficiency. We show that correctly specified models can generate samples with “wrongly” skewed residuals, even when the variance of the inefficiency process is nonzero. Both our bagging and bootstrap methods provide useful information about inefficiency and model parameters irrespective of whether residuals have skewness in the desired direction.     

浅析如何实现光的干涉

通过严密的数学推导,得出实现干涉需要三个条件:频率相同,振动方向相同和相位差恒定,具体的方法为分波面法与分振幅法;同时还需要满足两个附加条件:两束相干光的振幅不能相差太大和其光程差不能太大。     

外显义的发展及不可取消性考察

外显义是后格莱斯语用学理论发展的产物,体现了由语义和语用划分的争论向语用发展的趋势。可取消性是格莱斯提出用来判定会话含义的经典标准,部分学者认为它也可用来检验外显义。从意图的角度考察外显义发现,外显义是语言意义和语境意义组合而成的作为说话者意图表达的一个载体,因而可消除的只是听话者对说话者的意图进行推断的可能意图;而说话者的意图是不可取消的,因此蕴含明确意图的外显义是不能被取消的。     

Simple inference in multinomial and ordered logit

This paper provides some simple methods of interpreting the coefficients in multinomial logit and ordered logit models. These methods are summarized in Propositions concerning the magnitudes, signs, and patterns of partial derivatives of the outcome probabilities with respect to the exogenousvariables. The paper also provides an empirical example illustrating the use of these Propositions.     

Estimating systems of trending variables

A survey is given of some results on inference in cointegrated systems. We discuss some regression methods, and contrast them with the analysis of the vector autoregressive model. We discuss determination of cointegrating rank and estimation of parameters, as well as asymptotic inference. The problems are treated for 1(1) and for 1(2) variables.