具有Fabry缺项整函数系数的高阶齐次线性微分方程的复振荡

考虑具有Fabry缺项整函数系数的高阶齐次线性微分方程,得到的结果是J.K.Langley结果的扩展和补充。     

低浓度三分子模型的振幅方程

首先在考虑扩散流随时间变化的基础上,建立了低浓度三分子模型的双曲型反应－扩散方程及其波动方程,然后运用奇异微扰理论导出在Ｈｏｐｆ分岔点处的小振幅方程,为研究系统的能量演化奠定了理论基础     

基于流通基本方程的人民币研究

本文在一些假设的基础上推导出了我国货币流通的基本方程,并结合我国的当前实际情况,描述我国的人民币市场。     

GMM with Weak Identification

This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two important cases: linear instrumental variables regression and Euler equations estimation of the CCAPM. Numerical results for the CCAPM demonstrate that weak‐identification asymptotics explains the breakdown of conventional GMM procedures documented in previous Monte Carlo studies. Confidence sets immune to weak identification are proposed. We use these results to inform an empirical investigation of various CCAPM specifications; the substantive conclusions reached differ from those obtained using conventional methods.     

人口、经济总量、经济结构及技术进步对中国S02影响的实证分析——对Grossman污染分解方程的拓展

通过对Grossman污染分解方程进行拓展,加入了人口增长效应、收入效应,并对技术效应进行了进一步的分解,建立了新的污染物分解动态方程,以探讨影响污染物排放量的各种效应。在此基础之上,利用动态计量经济学的方法,以中国1985到2008年S02排放的数据进行分析,验证了人口增长效应是S02增加的重要原因;技术效应对减少S02的排放起到的作用逐渐加大。因此,在人口众多的中国,影响污染物排放的因素应该考虑到人口的规模效应。另外,国家需要继续加大能源利用技术和污染物处理技术的研发和应用来减少环境污染。     

A weighted test of internal symmetry

This study examines extensions of McNemar's Test with multinomial responses, and proposes a linear weighting scheme, based on the distance of the response change, that is applied to one of these extensions (Bowker's test). This weighted version of Bowker's test is then appropriate for ordinal response variables. A Monte Carlo simulation was conducted to examine the Type I error rate of the weighted Bowker's test for a cross-classification table based on a five-category ordinal response scale. The weighted Bowker's test was also applied to a data set involving change in student attitudes towards mathematics. The results of the weighted Bowker's test were compared with the results of Bowker's test applied to the same set of data.     

Testing for qualitative interaction of multiple sources of informative dropout in longitudinal data

Longitudinal studies suffer from patient dropout. The dropout process may be informative if there exists an association between dropout patterns and the rate of change in the response over time. Multiple patterns are plausible in that different causes of dropout might contribute to different patterns. These multiple patterns can be dichotomized into two groups: quantitative and qualitative interaction. Quantitative interaction indicates that each of the multiple sources is biasing the estimate of the rate of change in the same direction, although with differing magnitudes. Alternatively, qualitative interaction results in the multiple sources biasing the estimate of the rate of change in opposing directions. Qualitative interaction is of special concern, since it is less likely to be detected by conventional methods and can lead to highly misleading slope estimates. We explore a test for qualitative interaction based on simultaneous confidence intervals. The test accommodates the realistic situation where reasons for dropout are not fully understood, or even entirely unknown. It allows for an additional level of clustering among participating subjects. We apply these methods to a study exploring tumor growth rates in mice as well as a longitudinal study exploring rates of change in cognitive functioning for Alzheimer's patients.     

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.     

Detection of multiple undocumented change-points using adaptive Lasso

The problem of detecting multiple undocumented change-points in a historical temperature sequence with simple linear trend is formulated by a linear model. We apply adaptive least absolute shrinkage and selection operator (Lasso) to estimate the number and locations of change-points. Model selection criteria are used to choose the Lasso smoothing parameter. As adaptive Lasso may overestimate the number of change-points, we perform post-selection on change-points detected by adaptive Lasso using multivariate t simultaneous confidence intervals. Our method is demonstrated on the annual temperature data (year: 1902–2000) from Tuscaloosa, Alabama.     

次分数布朗运动下带交易费用的备兑权证定价

为了体现金融资产的长记忆性,采用次分数布朗运动刻画备兑权证标的资产价格变化的行为模式。利用随机分析理论和偏微分方程方法,建立了次分数布朗运动下带交易费用的备兑权证定价模型,进一步研究了定价模型的参数估计问题。最后,采用我国权证市场实际数据进行了实证分析,通过比较不同定价模型的结果说明了长记忆性和交易费用对定价结果有着显著的影响。