基于贝叶斯图模型方法的投资组合决策

提出基于贝叶斯思想的图模型结构检测方法:首先给出精度矩阵新的参数化方法,且通过MCMC方法给出算法设计,最后将该图模型方法应用于中国证券市场,研究在牛市和熊市下证券市场六大板块之间的条件相关性.实证结果表明:牛市和熊市下的行业板块图结构存在显著差异,熊市似乎有着更强的相关性.     

基于分位数回归的中国居民收入和消费的实证分析

阐述制约中国内需不足的几个主要因素,利用描述性统计和非参数方法重点考察中国居民收入差距的现状及其对扩大内需的重要影响.同时利用分位数回归技术对中国城镇和农村按收入等级划分的居民消费状况进行实证分析,结果表明:居民收入差距的确是制约中国内需不足的重要因素,不同收入阶层其边际消费倾向大不相同,并估算出这种消费倾向的具体值.从收入差距的视角提出了提高中国国内居民消费水平以便扩大内需的政策建议.     

中国居民食品消费的AIDS模型分析——以西部城镇地区为例

通过构建修正的AIDS模型,利用1992-2005年西部城镇地区居民食品消费的面板数据,实证研究了影响其食品消费的作用因素和机理.结果表明,西部城镇居民肉类和主粮消费支出份额较大,奶类和水产品支出份额较小;主粮支出和自价格弹性绝对值较小,而奶类和水产品支出和自价格弹性绝对值较大;家庭特征变量对居民食品消费的影响比较显著,最后得出相关启示.     

政府生产性基础设施投资对农业经济增长的影响——基于西安市的实证研究

根据西安市1988—2006年间的数据建立柯布一道格拉斯生产函数,在自回归分布滞后模型（ADL）下用最小二乘法（OLS）进行估计。实证分析结果显示：政府基础设施投资对西安市农业经济增长贡献显著,并且其作用远大于非基础设施投资的贡献,表明政府的生产性基础设施投资是西安市农业经济快速增长的关键因素。在此基础上,对西安市政府近期进行基础设施投资的重点方向提出了相关的政策建议。     

基于Granger因果图方法的物价水平国际间传递分析

目前,对Granger因果关系的研究大多数采用两变量Granger因果检验法,由于忽视其它重要变量的影响,常会导致虚假因果关系的出现。鉴此,采用Granger因果图模型方法分析中国及其主要贸易伙伴国(地区)间的物价传递,研究结果表明:美国在物价传递中发挥着主导作用,物价国际间传递存在一定的区域效应;除和中国香港地区存在即期因果关系外,中国对主要贸易伙伴国(地区)的物价水平基本无显著影响,中国既无输出通货膨胀也无输出通货紧缩。同时,样本期内中国物价水平呈现明显的外部"输入性"特征。因此,中国政府应采取措施应对国际的物价冲击,同时防范物价输入性引发的风险,以实现中国物价的稳定。     

Non parametric portmanteau tests for detecting non linearities in high dimensions

ABSTRACT

We propose two non parametric portmanteau test statistics for serial dependence in high dimensions using the correlation integral. One test depends on a cutoff threshold value, while the other test is freed of this dependence. Although these tests may each be viewed as variants of the classical Brock, Dechert, and Scheinkman (BDS) test statistic, they avoid some of the major weaknesses of this test. We establish consistency and asymptotic normality of both portmanteau tests. Using Monte Carlo simulations, we investigate the small sample properties of the tests for a variety of data generating processes with normally and uniformly distributed innovations. We show that asymptotic theory provides accurate inference in finite samples and for relatively high dimensions. This is followed by a power comparison with the BDS test, and with several rank-based extensions of the BDS tests that have recently been proposed in the literature. Two real data examples are provided to illustrate the use of the test procedure.     

On concurrence of several linear regressions and applications to problems of enzyme kinetics

In comparing several regressions E(y_ij) =α_i + β_ix_ij i = 1, 2, ..., k, j = 1,2, ..., n_i, researchers are generally interested in the following five problems: whether they have (1) equal slope, (2) equal intercept, (3) coincidence, (4) common intersection on X-axis, and (5) common intersection on (X,Y) - plane. Problems (1) - (3) can be put into the framework of the general linear hypothesis and the F-test can be used. However, problems (4) and (5) cannot be put into the general linear hypothesis because they are ratios of parameters. Hence, in this paper we consider the generalized likelihood ratio test for hypothesis testing. An application to an enzyme kinetics problem in Aniline Metabolism is demonstrated     

On the maximum number of constraints for s-symbol balanced arrays of strength t

Several upper bounds on the maximum number of constraints for s-symbol balanced arrays of strength t are given. Some bounds derived here include the results obtained by Rafter and Seiden (1974), Chopra (1982, 1983) and Saha, Mukerjee and Kageyama (1983).     

Construction of Confidence Intervals for the IVIean of a Population Containing Many Zero Values

The likelihood ratio method is used to construct a confidence interval for a population mean when sampling from a population with certain characteristics found in many applications, such as auditing. Specifically, a sample taken from this type of population usually consists of a very large number of zero values, plus a small number of nonzero values that follow some continuous distribution. In this situation, the traditional confidence interval constructed for the population mean is known to be unreliable. This article derives confidence intervals based on the likelihood-ratio-test approach by assuming (1) a normal distribution (normal algorithm) and (2) an exponential distribution (exponential algorithm). Because the error population distribution is usually unknown, it is important to study the robustness of the proposed procedures. We perform an extensive simulation study to compare the percentage of confidence intervals containing the true population mean using the two proposed algorithms with the percentage obtained from the traditional method based on the central limit theorem. It is shown that the normal algorithm is the most robust procedure against many different distributional error assumptions.