我国分季度GDP估算方法的研究

 近年来随着各国经济的不断融合和金融市场的日趋国际化,宏观经济的短期波动越来越受到各国的重视,季度GDP作为高质量的子年度统计数据可以说具有独特的研究价值,为此各国都在探讨季度GDP的核算方法,以获取更加及时、准确的统计数据。我国近两年经济波动较大,寻找及时、准确的高频数据进行短期经济分析具有重大意义。为此,本文借鉴国际上的Chou-Lin方法对我国季度GDP进行分产业的估算,并将估算结果与官方数据进行比较,发现估算结果与官方数据偏差极小,可以作为高频数据进行经济分析及预测。     

青岛一套表的实践与经验

 企业统计一套表一直是统计制度方法改革和实践中的焦点问题。本文通过对青岛市统计局企业一套表制度实施情况的案例分析,系统界定了企业一套表的概念和内涵,全面展现了企业一套表的运作过程,客观分析了制约企业一套表实施的主要原因,并提出了在现阶段实施企业统计一套表的条件和要求。     

地方政府支出与农村居民消费需求：基于1998-2007年省级 面板数据的经验分析

 政府支出对居民消费需求产生挤出抑或挤入效应是理论界最近关注的焦点之一。本文在理论诠释地方政府支出对农村居民消费需求的传导机制以及模型建立的理论框架下,通过建立地方政府支出与农村居民消费支出之间的个体固定效应变截距模型,运用我国31个省份1998-2007年的经验数据进行实证检验,结果表明：地方政府财政支农支出对农村居民消费具有显著的挤入效应,而转移性支出与农村居民消费的相关程度并不明显。基于理论分析和实证检验结果,本文最后针对地方政府扩大农村居民消费提出相关建议。     

ASSOCIATIONS BETWEEN AIR POLLUTION AND HOSPITAL VISITS FOR CARDIOVASCULAR DISEASES IN THE ELDERLY IN SYDNEY USING BAYESIAN STATISTICAL METHODS

Using generalized linear models (GLMs), Jalaludin  et al. (2006;  J. Exposure Analysis and Epidemiology   16 , 225–237) studied the association between the daily number of visits to emergency departments for cardiovascular disease by the elderly (65+) and five measures of ambient air pollution. Bayesian methods provide an alternative approach to classical time series modelling and are starting to be more widely used. This paper considers Bayesian methods using the dataset used by Jalaludin  et al.  (2006) , and compares the results from Bayesian methods with those obtained by Jalaludin  et al.  (2006) using GLM methods.     

VARIATIONAL BAYESIAN ANALYSIS FOR HIDDEN MARKOV MODELS

The variational approach to Bayesian inference enables simultaneous estimation of model parameters and model complexity. An interesting feature of this approach is that it also leads to an automatic choice of model complexity. Empirical results from the analysis of hidden Markov models with Gaussian observation densities illustrate this. If the variational algorithm is initialized with a large number of hidden states, redundant states are eliminated as the method converges to a solution, thereby leading to a selection of the number of hidden states. In addition, through the use of a variational approximation, the deviance information criterion for Bayesian model selection can be extended to the hidden Markov model framework. Calculation of the deviance information criterion provides a further tool for model selection, which can be used in conjunction with the variational approach.     

A new alternative to the standard F test for clustered data

The data collection process and the inherent population structure are the main causes for clustered data. The observations in a given cluster are correlated, and the magnitude of such correlation is often measured by the intra-cluster correlation coefficient. The intra-cluster correlation can lead to an inflated size of the standard F test in a linear model. In this paper, we propose a solution to this problem. Unlike previous adjustments, our method does not require estimation of the intra-class correlation, which is problematic especially when the number of clusters is small. Our simulation results show that the new method outperforms the existing methods.     

Confidence interval estimation of the difference between paired AUCs based on combined biomarkers

In many diagnostic studies, multiple diagnostic tests are performed on each subject or multiple disease markers are available. Commonly, the information should be combined to improve the diagnostic accuracy. We consider the problem of comparing the discriminatory abilities between two groups of biomarkers. Specifically, this article focuses on confidence interval estimation of the difference between paired AUCs based on optimally combined markers under the assumption of multivariate normality. Simulation studies demonstrate that the proposed generalized variable approach provides confidence intervals with satisfying coverage probabilities at finite sample sizes. The proposed method can also easily provide P-values for hypothesis testing. Application to analysis of a subset of data from a study on coronary heart disease illustrates the utility of the method in practice.     

Bayesian model comparison for compartmental models with applications in positron emission tomography

We develop strategies for Bayesian modelling as well as model comparison, averaging and selection for compartmental models with particular emphasis on those that occur in the analysis of positron emission tomography (PET) data. Both modelling and computational issues are considered. Biophysically inspired informative priors are developed for the problem at hand, and by comparison with default vague priors it is shown that the proposed modelling is not overly sensitive to prior specification. It is also shown that an additive normal error structure does not describe measured PET data well, despite being very widely used, and that within a simple Bayesian framework simultaneous parameter estimation and model comparison can be performed with a more general noise model. The proposed approach is compared with standard techniques using both simulated and real data. In addition to good, robust estimation performance, the proposed technique provides, automatically, a characterisation of the uncertainty in the resulting estimates which can be considerable in applications such as PET.     

组织支持、组织文化认同和情绪工作作用机制研究

以服务行业工作人员为研究对象,通过实证分析对组织支持、组织文化认同和情绪工作间的作用机制进行研究。结果表明：同事支持、上级支持和企业支持对表层行为有显著负向影响,上级支持和企业支持对深层行为有显著正向影响;组织文化认同的行为性认同维度对表层行为有抑制作用,认知性认同、情感性认同和行为性认同则能有效激发员工的深层行为;组织支持通过组织文化认同的部分中介作用对表层行为和深层行为产生影响。     

机构投资者异质性的上市公司股利政策研究

 本文从现金股利政策的角度来研究机构投资者的投资行为和参与上市公司治理的情况。首先回顾了该方面的相关文献,以信号理论为基础,分析机构投资者与现金股利政策可能存在的关系;接着对机构投资者的基本特征以及我国机构投资者和上市公司现金股利政策的概况及特点进行了总结;最后本文以 2007至2010 年的机构投资者持股数据与上市公司现金股利政策数据进行统计分析、回归分析,实证检验了机构投资者异质性与上市公司股利政策之间的关系。