出生婴儿性别比偏高原因的系统分析

在回顾近10年来国内学者对出生婴儿性别比偏高原因研究的基础上,利用出生婴儿性别比偏高流程图,对出生婴儿性别比偏高的原因作了系统分析。提出并区分了“时点出生性别比”与“时期出生性别比”的概念。认为,出生婴儿性别比偏高政策因素不是主要因素,性别选择技术是主要原因,性别比偏高是社会、经济、文化等因素共同作用的结果。并提出两点标本兼治的对策建议。     

A Bayesian analysis on time series structural equation models

Structural equation models (SEM) have been extensively used in behavioral, social, and psychological research to model relations between the latent variables and the observations. Most software packages for the fitting of SEM rely on frequentist methods. Traditional models and software are not appropriate for analysis of the dependent observations such as time-series data. In this study, a structural equation model with a time series feature is introduced. A Bayesian approach is used to solve the model with the aid of the Markov chain Monte Carlo method. Bayesian inferences as well as prediction with the proposed time series structural equation model can also reveal certain unobserved relationships among the observations. The approach is successfully employed using real Asian, American and European stock return data.     

时变空间权重矩阵面板数据模型稳健LM检验有效性研究

空间权重矩阵是描述个体间空间关系的重要工具,通常基于个体间的地理距离构造不随时间而改变的空间权重矩阵。然而,当个体间的空间关系源自经济/社会/贸易距离或人口流动性/气候等特征时,空间权重矩阵本质上可能将随时间而改变。由此,本研究提出时变空间权重矩阵面板数据模型的稳健LM检验。大量Monte Carlo模拟结果显示：从检验水平和功效角度来看,基于误设的非时变空间权重矩阵的稳健LM检验存在较大偏差,但是基于时变空间权重矩阵的稳健LM检验能够有效地识别面板数据中的空间关系类型。尤其是,在时间较长和个体较多等情况下,时变空间权重矩阵的稳健LM检验功效更高。     

A Nonparametric Test of the Predictive Regression Model

This article considers testing the significance of a regressor with a near unit root in a predictive regression model. The procedures discussed in this article are nonparametric, so one can test the significance of a regressor without specifying a functional form. The results are used to test the null hypothesis that the entire function takes the value of zero. We show that the standardized test has a normal distribution regardless of whether there is a near unit root in the regressor. This is in contrast to tests based on linear regression for this model where tests have a nonstandard limiting distribution that depends on nuisance parameters. Our results have practical implications in testing the significance of a regressor since there is no need to conduct pretests for a unit root in the regressor and the same procedure can be used if the regressor has a unit root or not. A Monte Carlo experiment explores the performance of the test for various levels of persistence of the regressors and for various linear and nonlinear alternatives. The test has superior performance against certain nonlinear alternatives. An application of the test applied to stock returns shows how the test can improve inference about predictability.     

健康对家庭时间分配的影响

本文利用CHNS数据探讨健康因素对已婚男女时间分配所产生的影响。实证结果显示健康状况受损与自身的市场工作时间呈负相关关系;健康受限使得城市男女趋向于减少家务劳动时间的承担,而农村样本却表现了与城市同类人群相反的方向。而且值得关注的是,健康因素对于男女时间分配的影响效应是不一样的。     

Finite sample properties of ml and reml estimators in time series regression models with long memory noise

Finite sample properties of ML and REML estimators in time series regression models with fractional ARIMA noise are examined. In particular, theoretical approximations for bias of ML and REML estimators of the noise parameters are developed and their accuracy is assessed through simulations. The impact of noise parameter estimation on performance of t -statistics and likelihood ratio statistics for testing regression parameters is also investigated.     

Importance sampling for signal extraction model in time series analysis

In time series analysis, signal extraction model (SEM) is used to estimate unobserved signal component from observed time series data. Since parameters of the components in SEM are often unknown in practice, a commonly used method is to estimate unobserved signal component using the maximum likelihood estimates (MLEs) of parameters of the components. This paper explores an alternative way to estimate unobserved signal component when parameters of the components are unknown. The suggested method makes use of importance sampling (IS) with Bayesian inference. The basic idea is to treat parameters of the components in SEM as a random vector and compute a posterior probability density function of the parameters using Bayesian inference. Then IS method is applied to integrate out the parameters and thus estimates of unobserved signal component, unconditional to the parameters, can be obtained. This method is illustrated with a real time series data. Then a Monte Carlo study with four different types of time series models is carried out to compare a performance of this method with that of a commonly used method. The study shows that IS method with Bayesian inference is computationally feasible and robust, and more efficient in terms of mean square errors (MSEs) than a commonly used method.     

理解的伦理学

人类之间的相互理解现在具有十分重要的意义,而真正地实现理解需要运用复杂性认识方法。看到每个人都具有潜在的多重人格,在随着历史背景条件飘移中实现某一种自身,因此我们不应把犯有罪过的人简化为他们一时犯下的罪过,而看到他们改变的可能性。客观的理性的理解还应辅之以主观的推己及人的理解,才能产生同情。了解产生不理解的各种根源自觉地加以克服,从而力争达到人类的相互宽容和团结。     

在：从“存在”义动词到时问副词——兼论“正在”之形成

“在”由处所动词发展出时间副词的用法,句法上,经历了从[V＋VP]到[Adv．＋VP]的重新分析;语义上,经历了表“存在”→表“在……状态之中”→表“动作进行或状态持续”的虚化。文章通过大量例证,对“在”跨方所一时态范畴演化过程作详尽的论述,同时讨论时间副词“正在”的形成过程。提出时间副词“正在”是“正”和“在”叠加使用的结果,“在”突出当前一段时间动作行为的存在状况,是时段概念;而“正在”的语法性质与“正”更接近,强调动作行为发生的时间位置,是时点概念。     

复杂性及其研究的意味

作为一种难以被科学和哲学严格界定（因其自身的复杂）的理论范式,“复杂性”虽然在语义上没有获得各学科都能够普遍接受的通论,但它总是伴随着我们,复杂性是实在（reality）的一种深刻特性。“复杂性”意味着存在着不熟悉、不理解;意味着困难、障碍和挑战;同时也意味着可能和开放。研究复杂性意味着思想范式的革命,这在哲学、科学和人类思维的变革中表现得最为深刻。这是哲学的自我救赎,也是人类破除科学迷信的路径,最重要的是复杂性思维将引导我们进入新文明时代。