非参数统计视角下的异方差检验设计及其应用

以数据生成过程为导向,探讨了异方差来源的基本类型;依据非参数统计的基本思想,设计了切实可行的Mood方差检验方法与平方秩检验方法,并针对异方差来源类型,分析了相应的检验思路。蒙特卡罗模拟表明,Mood方差检验方法在异方差检验方面具有很高的检验效力;平方秩检验方法在异常经济现象情形下检验效力较低,而在其他情形下检验效力很高。同时,进一步阐释了纠正异方差的基本逻辑。     

时变Copula模型的非参数推断

运用Copula模型研究金融变量之间的相关结构,是近年来金融分析中的一个热点,如何估计Copula模型中的时变参数则是一个重点和难点问题。本文从非参数建模思想为切入点,提出经验分布函数—局部极大似然法(ECDF-LML)估计Copula函数中的时变参数,研究了Copula模型参数是否时变的统计假设检验问题。最后通过大量随机模拟研究验证了本文所提出的方法较DCC-MGARCH方法在刻画随机变量动态相关性方面更具优越性且很稳健。     

带未知异方差广义空间模型的有效估计

空间单元大小以及其它的经济特征上的差异,常常会导致空间异方差问题。本文给出了广义空间模型异方差问题的三种不同估计方法。第一种方法是将异方差形式参数化,来克服自由度的不足,使用ML估计进行实现。而针对异方差形式未知时,分别采用了基于2SLS的迭代GMM估计和更加直接的MCMC抽样方法加以解决,特别是MCMC方法表现得更加优美。蒙特卡罗模拟表明,给定异方差形式条件下, ML估计通过异方差参数化的方法依然可以获得较好的估计效果。而异方差形式未知的情况下,另外两种方法随着样本数的增大时也可以与ML的估计结果趋于一致。     

基于自回归条件异方差模型的上证指数收益波动分析

股票市场的股价波动会引起股价指数的涨跌。本文基于计量经济学的自回归条件异方差模型,对上证指数2007年以来的1276个交易日的样本数据进行实证研究。结果表明:上证指数收益序列具有尖峰厚尾特征和波动的时段集群特征,适合利用自回归条件异方差模型进行分析及预测。研究结果为各方从不同角度把握上证指数收益波动的规律提供了股票投资理论和方法。     

非线性计量经济模型的非参数估计方法研究

本文提出使用核估计的方法构造平滑转移模型(STR)的非参数模拟最大似然估计(NPSML),给出了NPSML估计量的构造方法、渐近性质以及相应的核函数和窗宽的选择准则,并利用滑动窗宽算法对估计量的构造过程进行了改进。通过Monte Carlo实验证明,该方法是可靠的,并且当误差项存在序列相关时,此种估计量是稳健的。     

一种估计和检验排序模型中结构变化的方法及应用

研究目标：给出一种估计和检验排序模型中结构变化的方法。研究方法：在排序模型中引入平滑转换函数来描述结构变化,在此基础上构建拉格朗日乘子统计量检验模型中的结构变化,并使用极大似然方法估计模型。研究发现：拉格朗日乘子统计量具有标准的渐近卡方分布,并且该统计量对误差项的不同分布形式具有较好的稳健性;极大似然估计量具有一致性和渐近正态性;应用本文的方法分析居民收入和幸福关系,发现收入对幸福的影响存在显著的非线性结构变化特征,当收入增长超过社会收入分布的80%分位数时,收入对幸福的作用会减弱。研究创新：提出了一种新的并且是比较简便的估计和检验排序模型中结构变化的方法。研究价值：这种新的方法可以广泛应用于主观评价问题中的结构变化分析。     

带未知异方差与半线性结构Tobit模型的半参数估计

本文将Tobit模型扩展至同时带未知条件异方差与半线性结构回归函数的场合,并提出一种计算简便的半参数二步估计法。该方法的关键之处在于连续两次施以成对相减变换,并先后消去第一步所得被解释变量非参数条件分位函数中的两类非线性冗余成分(非线性回归函数部分与未知异方差结构)。文章证明了估计量的n-一致性与渐近正态性,并通过Monte Carlo模拟研究了分位点对的选择、扰动项分布类型与样本删尾程度等因素对估计量小样本性质的影响。最后通过国内居民医疗服务利用不平等的实例验证了本文所提的方法。     

半参数变系数空间滞后模型的截面极大似然估计

在线性参数空间滞后模型中,解释变量的系数一般假设为固定常数,本文首先放松了这种假设,将解释变量的系数设定为某一变量的未知函数,提出一类全新的半参数变系数空间滞后模型;其次导出了该模型的截面极大似然估计,并证明了该估计的一致性;最后用蒙特卡洛数值模拟方法考察了该估计在小样本条件下的性质,数值模拟结果显示我们提出的估计方法在小样本条件下依然有优良的表现。     

均值—方差模型在个人理财中的应用及优化

本文简要论述了均值—方差模型及最优投资组合,并在此基础上引入交易成本及无风险资产因素对Markowitz均值—方差模型进行优化,使得经典的均值—方差模型更具适用性。     

Logit模型改进及其在财务困境预警中的应用

财务预警理论起源于国外并一直都是投资和财务管理领域的重要研究方向。随着我国市场经济体制的建立和完善,也需要对上市公司的财务状况做出评价,而对其应用也因之发展。资料表明,第一个进行公开发表的关于我国上市公司财务困境预警实证研究的是陈静(1999),她分别采用Bevaer的单变量分析和Alntlna的多元判别分析(遵循Fihser准则),通过ST企业和非ST企业(共54家)的年度财务指标建立了判别模型。张玲(2000)以14个行业共120家     

应该用哪一个模型来描述中国货币市场利率的动态变化

本文论证了双曲模型是描述中国货币市场利率动态变化的最佳单因子利率模型。由极大似然估计可以得到单因子利率模型的边际密度函数。双曲模型的边际密度和非参数估计得到的边际密度函数拟合较好,其表现远远优于几个常见的利率模型（CIR、CKLS和AG模型）。与较一般的Ait-Sahalia模型相比差别很小,但参数形式得到简化,似然比检验也支持这一点。双曲模型在刻画利率的均值回复特征方面还克服了AG模型的不足。     

Another Approach to Data Envelopment Analysis in Noisy Environments: DEA+

In this paper a DEA+ labeled approach for efficiency measurement in the stochastic case is presented along with a consistency proof and some preliminary evidence illustrating the small sample performance. DEA+ can basically handle multi-output technologies like standard DEA but allows to filter noise, that might have disturbed production and unlike a related approach does not require panel data. Consistency of DEA+ relies on the assumption of i.i.d. distributed and bounded noise and requires radial efficiency measurement. First Monte Carlo experiments show that a DEA+ based average inefficiency estimator performs well for samples of size n=100 in one-output, two-input settings compared to the corresponding Stochastic Frontier Estimator. Sensitivity of DEA+ performance with respect to parametrization of noise is weak, but higher noise contribution requires much larger sample size for satisfactory results.     

Estimation of Non-Stationary Social Accounting Matrix Coefficients with Supply-Side Information

Given aggregated data, a framework for estimating the entries of a social accounting matrix (SAM), or any large matrix of expenditures, trade or income flows, is developed. Under this framework it is possible to evaluate the contribution of structural and supply-side information, as well as policy variables, within the generalized context of a non-stationary SAM. Inference and diagnostic properties are developed as well. This new estimator can be viewed as a generalized maximum likelihood estimator. Stationary and non-stationary estimates of the US SAM for the years 1987-1994 together with the effects of supply-side variables are analyzed.     

Adaptive Estimation of a Conditional Density

The authors consider the problem of estimating a conditional density by a conditional kernel density estimate when the error associated with the estimate is measured by the L₁‐norm. On the basis of the combinatorial method of Devroye and Lugosi ( 1996 ), they propose a method for selecting the bandwidths adaptively and for providing a theoretical justification of the approach. They use simulated data to illustrate the finite‐sample performance of their estimator.     

Three Sides of Smoothing: Categorical Data Smoothing, Nonparametric Regression, and Density Estimation

The past forty years have seen a great deal of research into the construction and properties of nonparametric estimates of smooth functions. This research has focused primarily on two sides of the smoothing problem: nonparametric regression and density estimation. Theoretical results for these two situations are similar, and multivariate density estimation was an early justification for the Nadaraya-Watson kernel regression estimator.
A third, less well-explored, strand of applications of smoothing is to the estimation of probabilities in categorical data. In this paper the position of categorical data smoothing as a bridge between nonparametric regression and density estimation is explored. Nonparametric regression provides a paradigm for the construction of effective categorical smoothing estimates, and use of an appropriate likelihood function yields cell probability estimates with many desirable properties. Such estimates can be used to construct regression estimates when one or more of the categorical variables are viewed as response variables. They also lead naturally to the construction of well-behaved density estimates using local or penalized likelihood estimation, which can then be used in a regression context. Several real data sets are used to illustrate these points.     

Asymptotics of the Non-parametric Function for B-splines-based Estimation in Partially Linear Models

We consider least squares method for partially linear models based on polynomial splines. We derive the asymptotic property for the estimator, focusing on the estimation of the non-parametric function, in particular whether and how the estimation of the linear part will affect the non-parametric part (the converse relation, that is, how the linear part will be affected by the non-parametric part is much better known, which we will also review). One important goal along the way is to clarify the role of projection in semiparametric models, which was nevertheless a classical trick for proving the asymptotic normality of the linear part. A crucial question we try to answer is whether projection plays any role in the estimation of the non-parametric function. The answer is both positive and negative depending on the direction along which to assess asymptotic normality. The style of writing of the paper is somewhat expository, and it also contains several new results not found in the current literature. Finally, we demonstrate in our numerical studies that construction of the pointwise confidence intervals for the non-parametric function motivated by our theory improves upon those constructed by pretending the linear part is known.     

经济适用房空间布局分析——以北京市为例

经济适用房的建设和布局对城市空间结构和居住者出行具有重要影响。首先采用Kernel密度估计方法计算了北京不同时期经济适用房在不同空间上的建设规模密度,分析其空间布局特征;其次,从经济适用房与地价、交通和主要就业地的相互关系分析经济适用房空间布局的演变。研究表明,北京经济适用房空间布局经历了逐步调整的过程,前期主要分布在城市北部城乡结合地带,后期考虑居民交通出行和到工作地方便等因素,在城市四周较均匀的配置和建设。总体来说,北京市经济适用住房基本分布在城市郊区,就业地与居住地相距较远的矛盾依然突出。建议今后经济适用房空间布局应更加注重主要就业地和居民交通出行的最佳匹配。     

半参数变系数空间误差回归模型的估计

研究目标：克服半参数变系数回归模型中误差项可能存在的空间相关性问题。研究方法：提出一类新的半参数变系数空间误差回归模型,并构造其截面似然估计。研究发现：在小样本条件下,模型估计量具有良好的表现,其精度随着样本容量的增加而提高;应用该方法分析我国资源禀赋与地方公共品供给之间的相互关系,进一步证实了模型较强的适用性。研究创新：证明了估计量的一致性与渐近正态性,并通过蒙特卡洛模拟考察了估计方法的小样本表现。研究价值：新方法对于其他结构的半/非参数空间计量模型理论研究具有推广价值,其估计技术在经济、管理等学科中具有应用价值。     

存在自相关时自回归模型的估计和检验

讨论存在自相关情况下自回归模型中随机解释变量的内生性,指出目前的计量经济理论所存在的问题,证明了随机误差存在自相关情况下一阶自回归模型和高阶自回归模型的随机解释变量与随机误差都不相关,同时改进了自回归模型的估计和检验方法。     

Periodic inspection plans: The case of Weibull distribution

In estimating the lifetime distribution of a product, the efficiency of a periodic inspection plan vis-á-vis with the continuous inspection plan is discussed. The periodic inspection plan envisages inspecting the units in the sample periodically at m successive equi-distant time points, for a given choice of m. The issue of choosing the value of m is also discussed. The discussions are carried out when the underlying lifetime distribution is Weibull.