全球化背景下我国主导文化面临的受动性冲击

全球化的发展 ,一方面促进了各国文化的交流 ,另一方面也带来了各国文化的冲突 (其中异质文化的冲突尤为突出 )。全球化呈现出来的西方文化全球扩张的特征 ,必然会对我国的社会主义主导文化形成一定的冲击。从思想文化、制品文化、网络文化、生活文化等文化层面 ,客观准确地分析这种冲击的具体性状 ,有助于增强我国文化建设的紧迫感 ,有重点地推进中国特色社会主义先进文化建设。     

电子新闻媒介在信息传播中CIA语用理论

在信息传递中传者、受众、传播内容三要素以及他们之间的关系决定传播效果。作者提出的“CIA”语用理论,是针对广播、电视媒介,主要研究主持人、受众、节目内容三要素的关系,把这三个要素看成三个端点,组成一个三角形,如果拉近边线的距离,缩小三角形的面积,就能明显提高传播效果。     

SiCp/A1FGM微观组织对冲击韧性的影响

功能梯度材料具有良好的力学性能，有广泛的应用前景。通过观测SiCp/AI功能梯度复合材料的微观组织，并对增强体尺寸分别为5、7、10μm的SiCp/AI功能梯度复合材料的冲击韧性进行了测试。结果表明：显微组织显示各梯度层之间界面模糊，粘结良好，没有明显缺陷；5μmSiCp/A1FGM的冲击韧性较好；在合理的增强体尺寸范围内，颗粒尺寸越小，材料冲击韧性越好。     

Asymptotic Distributions of Unit-Root Tests When the Process Is Nearly Stationary

Several test criteria are available for testing the hypothesis that the autoregressive polynomial of an autoregressive moving average process has a single unit root. Schwert (1989), using a Monte Carlo study, investigated the performance of some of the available test criteria. He concluded that the actual levels of the test criteria considered in his study are far from the specified levels when the moving average polynomial also has a root close to 1. This article studies the asymptotic null distribution of the test statistics for testing p = 1 in the model Y_t = pY _t-1 + e _t – 0e _t-1 as 0 approaches 1. It is shown that the test statistics differ from one another in their asymptotic properties depending on the rate at which 0 converges to 1.     

Combining Economic Forecasts

A method for combining forecasts may or may not account for dependence and differing precision among forecasts. In this article we test a variety of such methods in the context of combining forecasts of GNP from four major econometric models. The methods include one in which forecasting errors are jointly normally distributed and several variants of this model as well as some simpler procedures and a Bayesian approach with a prior distribution based on exchangeability of forecasters. The results indicate that a simple average, the normal model with an independence assumption, and the Bayesian model perform better than the other approaches that are studied here.     

Bootstrap confidence regions computed from autoregressions of arbitrary order

Given a linear time series, e.g. an autoregression of infinite order, we may construct a finite order approximation and use that as the basis for confidence regions. The sieve or autoregressive bootstrap, as this method is often called, is generally seen as a competitor with the better-understood block bootstrap approach. However, in the present paper we argue that, for linear time series, the sieve bootstrap has significantly better performance than blocking methods and offers a wider range of opportunities. In particular, since it does not corrupt second-order properties then it may be used in a double-bootstrap form, with the second bootstrap application being employed to calibrate a basic percentile method confidence interval. This approach confers second-order accuracy without the need to estimate variance. That offers substantial benefits, since variances of statistics based on time series can be difficult to estimate reliably, and—partly because of the relatively small amount of information contained in a dependent process—are notorious for causing problems when used to Studentize. Other advantages of the sieve bootstrap include considerably greater robustness against variations in the choice of the tuning parameter, here equal to the autoregressive order, and the fact that, in contradistinction to the case of the block bootstrap, the percentile t version of the sieve bootstrap may be based on the 'raw' estimator of standard error. In the process of establishing these properties we show that the sieve bootstrap is second order correct.     

Program Evaluation and Causal Inference With High‐Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data‐rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function‐valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced‐form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post‐regularization and post‐selection inference that are uniformly valid (honest) across a wide range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced‐form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment‐condition framework, which arises from structural equation models in econometrics. Here, too, the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function‐valued) parameters within this general framework where any high‐quality, machine learning methods (e.g., boosted trees, deep neural networks, random forest, and their aggregated and hybrid versions) can be used to learn the nonparametric/high‐dimensional components of the model. These include a number of supporting auxiliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity‐based estimation of regression functions for function‐valued outcomes.     

浅析中亚的地缘态势及对中国的影响

中亚地区地缘战略极其重要，具有现实的和潜在的地缘政治价值。冷战后，各大国纷纷介入，使中亚成为大国和地区力量角逐的又一个新的焦点。由于中亚各国是中国的近邻，因此，在中亚展开的激烈地缘竞争态势必然与中国的战略利益密切联系，对中国的边疆稳定、能源安全造成重大的影响。     

长三角的产业集聚及其省区特征、同构绩效——一个基于长三角产业集聚演化的视角

产业集聚是长三角产业发展的重要特征.文章根据影响产业集聚的一般因素,结合长三角区域经济发展实际,研究了长三角产业集聚的动力机制、空间演化路径和集聚绩效.文章认为,产业的集聚动力在于地区的竞争优势,新经济地理学理论中产业集聚的影响因素则加深和放大了地区的产业集聚过程;长三角的历史沿革、区位优势和中国转轨经济的体制改革特征决定了长三角省区经济空间演化的特征是产业同构;这种以产业同构为主要内容的长三角产业集聚并没有表现为省区经济之间的"重复建设"或者是"恶性竞争",基于在世界经济格局中廉价劳动力比较分工优势的长三角的产业集聚引起的外部性、市场扩大效应和价格指数效应成为区域产业同构具有正面绩效的支撑因素,但地区的工业化进程对相关产业的绩效存在影响.文章的实证研究支持上述结论.     

Religion,social milieu,and the contraceptive revolution

This study examines how the social environment of religious congregations affects the spread of contraceptive use in developing contexts, using Mozambique as a case study. Analysis of qualitative data collected in urban areas of that country in 1998-99 and of the data from the 1997 Mozambique Demographic and Health Survey suggests that, in urban areas, the environment of more socioculturally diverse and inclusive Roman Catholic and mission-based Protestant congregations is more propitious to the spread and legitimization of modern contraception than the milieu of smaller, relatively homogeneous, independent churches. In rural areas, however, sociocultural diversity within and across different religious denominations is minimal, and membership in any formal congregation offers an advantage in contraceptive learning.