VaR样本分位数估计的偏差改进

本文指出了人们通常所使用的VaR样本分位数估计量会产生高估或低估的现象,并分析了产生这些现象的原因,提出在样本较大的情况下利用加权样本分位数估计量去估计VaR,在样本较小的情况下用基于Bootstrap方法的样本分位数估计量去估计VaR。数值模拟的结果表明,这些估计方法的估计精度得到了较好地改进。最后,运用这两种分位数估计量来估计两支股票(招商银行、中国石化)的日对数回报序列的VaR值,并比较它们的风险估计量的大小。     

具有异质性非参数时间趋势的面板数据模型的估计

本文为一类具有异质性非参数时间趋势的面板数据模型提出了一种简单估计方法。基于局部多项式回归的思想,首先去除数据中的时间趋势成分,然后由最小二乘法来估计公共系数,同时得到时间趋势函数的非参数估计。在一些正则条件下,研究了这些估计量的渐近性质,即在时间维度T和横截面维度n同时趋向无穷时,建立了各个估计量的渐近相合性和渐近正态性。最后通过蒙特卡洛模拟,考查了这种估计方法的有限样本性质。     

时空动态半参数变系数随机效应面板模型的估计

研究目标：克服半参数变系数面板模型中扰动项和因变量存在时空动态性问题。研究方法：提出一类更加一般化的时空动态半参数变系数随机效应面板模型，并构建截面似然估计量。研究发现：估计量具有良好的小样本性质，估计误差随着样本总量的提高而减小，在Case空间矩阵下，空间滞后和时空滞后系数的估计精度随空间复杂度的增大而降低，用该方法分析我国外商直接投资、知识产权保护与经济增长关系，进一步证实了模型的适用性。研究创新：证明了估计量满足一致性和渐近正态性，数值模拟考察了估计量的小样本性质。研究价值：拓展了现有半参数变系数空间面板模型的形式，增强了模型的适用性和解释力，有益于经济问题实证研究的开展。     

层级数据空间误差自回归模型的估计方法研究

本文建立同时考虑空间误差自回归和嵌套随机效应误差分量的层级数据空间误差自回归模型,并推导最优权重GMM估计量,对空间自回归系数和误差项的方差进行估计。然后,定义对应的FGLS估计量,对层级数据空间误差自回归模型的总体回归系数进行估计。通过蒙特卡洛模拟,验证了所提出模型估计量的有限样本性质。模拟结果表明,本文提出的最优权重GMM估计量以及总体回归系数的GMM FGLS估计量有很好的小样本性质。     

IV估计的最优工具变量选取方法

IV估计的有限样本性质对工具变量的选取十分敏感,尤其是存在弱工具变量的情形。本文在Donald和Newey(2001)的基础上研究了常用的IV估计———2SLS的最优工具变量选取方法。首先通过对2SLS估计量进行Nagar分解,从理论上推导出估计量的近似MSE表达式;根据这一表达式,提出IV估计的最优工具变量选取准则,并证明选取准则的渐近有效性。模拟结果表明,本文提出的工具变量选取准则能够极大地改善2SLS估计量的有限样本表现。本研究为实证中面临的工具变量选择问题提供了理论依据。     

时变混合Copula模型的非参数估计及应用研究

针对传统动态混合Copula参数建模所存在的缺陷,本文提出了混合Copula的非参数建模方法,即不对模型的参数进行任何形式的设定,而假设参数为时间的函数,运用局部极大似然估计法来对参数进行估计。我们推导了非参数估计量的渐近正态性质、讨论了估计量的偏差与方差的大小,并运用交叉验证法给出最优带宽的选择。有限样本下的蒙特卡洛模拟显示,时变混合Copula的非参数估计具有良好的统计性质。最后,将该方法应用于股市间尾部相依特征的研究,发现我国股市与美、日、港、英股市间的左右尾部相依性呈现明显的时变性和非对称性。     

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。     

估计MS-DSGE模型脉冲响应函数的EM算法及其应用

研究目标：考察不同区制下外生冲击对中国宏观经济的非对称性效应。研究方法：引入两状态的Markov区制转换过程建立MS-DSGE模型,并基于MS-DSGE模型的Markov区制转换动态因子模型的表示提出了估计MS-DSGE模型脉冲响应函数的极大似然估计EM算法。研究发现：本文提出的估计方法具有良好的有限样本性质和收敛性,参数估计量具有渐近正态分布。实证分析发现,应持续施行扩张性政策以刺激经济稳定增长,对冲挤占效应以及稳定物价水平。尤其,当经济处于“衰退”区制时,政府应实施及时有效的调控政策刺激经济运行区制的转移。研究创新：与Bayesian分析方法比较,本文提出的估计方法避免了对数线性化MS-DSGE模型的随机奇异性以及对先验分布的设定和观测变量选取的非稳健性。研究价值：提出了一种估计MS-DSGE模型脉冲响应函数的方法。     

似无关模型在多响应参数估计中的应用

在复杂产品质量改进或过程优化中,通常需要拟合出多个响应与因子的经验模型,响应间的相关性会影响模型参数的估计精度。介绍了似无关模型参数估计的基本方法,并与最小二乘方法进行对比,算例表明,当响应见相关性较强,似无关模型估计量的性质优于单方程的最小二乘估计量。     

Market microstructure noise,integrated variance estimators,and the accuracy of asymptotic approximations

A growing literature has been advocating consistent kernel estimation of integrated variance in the presence of financial market microstructure noise. We find that, for realistic sample sizes encountered in practice, the asymptotic results derived for the proposed estimators may provide unsatisfactory representations of their finite sample properties. In addition, the existing asymptotic results might not offer sufficient guidance for practical implementations. We show how to optimize the finite sample properties of kernel-based integrated variance estimators. Empirically, we find that their suboptimal implementation can, in some cases, lead to little or no finite sample gains when compared to the classical realized variance estimator. Significant statistical and economic gains can, however, be recovered by using our proposed finite sample methods.     

Testing and imposing Slutsky symmetry in nonparametric demand systems

Maximization of utility implies that consumer demand systems have a Slutsky matrix which is everywhere symmetric. However, previous non- and semi-parametric approaches to the estimation of consumer demand systems do not give estimators that are restricted to satisfy this condition, nor do they offer powerful tests of this restriction. We use nonparametric modeling to test and impose Slutsky symmetry in a system of expenditure share equations over prices and expenditure. In this context, Slutsky symmetry is a set of nonlinear cross-equation restrictions on levels and derivatives of consumer demand equations. The key insight is that due to the differing convergence rates of levels and derivatives and due to the fact that the symmetry restrictions are linear in derivatives, both the test and the symmetry restricted estimator behave asymptotically as if these restrictions were (locally) linear. We establish large and finite sample properties of our methods, and show that our test has advantages over the only other comparable test. All methods we propose are implemented with Canadian micro-data. We find that our nonparametric analysis yields statistically significantly and qualitatively different results from traditional parametric estimators and tests.     

On Maximum Likelihood Estimation of Dynamic Panel Data Models

We analyse the finite sample properties of maximum likelihood estimators for dynamic panel data models. In particular, we consider transformed maximum likelihood (TML) and random effects maximum likelihood (RML) estimation. We show that TML and RML estimators are solutions to a cubic first‐order condition in the autoregressive parameter. Furthermore, in finite samples both likelihood estimators might lead to a negative estimate of the variance of the individual‐specific effects. We consider different approaches taking into account the non‐negativity restriction for the variance. We show that these approaches may lead to a solution different from the unique global unconstrained maximum. In an extensive Monte Carlo study we find that this issue is non‐negligible for small values of T and that different approaches might lead to different finite sample properties. Furthermore, we find that the Likelihood Ratio statistic provides size control in small samples, albeit with low power due to the flatness of the log‐likelihood function. We illustrate these issues modelling US state level unemployment dynamics.     

GMM estimation of spatial autoregressive models with moving average disturbances

In this paper, we introduce the one-step generalized method of moments (GMM) estimation methods considered in Lee (2007a) and Liu, Lee, and Bollinger (2010) to spatial models that impose a spatial moving average process for the disturbance term. First, we determine the set of best linear and quadratic moment functions for GMM estimation. Second, we show that the optimal GMM estimator (GMME) formulated from this set is the most efficient estimator within the class of GMMEs formulated from the set of linear and quadratic moment functions. Our analytical results show that the one-step GMME can be more efficient than the quasi maximum likelihood (QMLE), when the disturbance term is simply i.i.d. With an extensive Monte Carlo study, we compare its finite sample properties against the MLE, the QMLE and the estimators suggested in Fingleton (2008a).     

Determination of Discrete Spectrum in a Random Field

We consider a two dimensional frequency model in a random field, which can be used to model textures and also has wide applications in Statistical Signal Processing. First we consider the usual least squares estimators and obtain the consistency and the asymptotic distribution of the least squares estimators. Next we consider an estimator, which can be obtained by maximizing the periodogram function. It is observed that the least squares estimators and the estimators obtained by maximizing the periodogram function are asymptotically equivalent. Some numerical experiments are performed to see how the results work for finite samples. We apply our results on simulated textures to observe how the different estimators perform in estimating the true textures from a noisy data.     

Multivariate Hill Estimators

We propose two classes of semi‐parametric estimators for the tail index of a regular varying elliptical random vector. The first one is based on the distance between a tail probability contour and the observations outside this contour. We denote it as the class of separating estimators. The second one is based on the norm of an arbitrary order. We denote it as the class of angular estimators. We show the asymptotic properties and the finite sample performances of both classes. We also illustrate the separating estimators with an empirical application to 21 worldwide financial market indexes.     

Heteroskedasticity-robust semi-parametric GMM estimation of a spatial model with space-varying coefficients

Heteroskedasticity-robust semi-parametric GMM estimation of a spatial model with space-varying coefficients. Spatial Economic Analysis. The spatial model with space-varying coefficients proposed by Sun et al. in 2014 has proved to be useful in detecting the location effects of the impacts of covariates as well as spatial interaction in empirical analysis. However, Sun et al.’s estimator is inconsistent when heteroskedasticity is present – a circumstance that is more realistic in certain applications. In this study, we propose a kind of semi-parametric generalized method of moments (GMM) estimator that is not only heteroskedasticity robust but also takes a closed form written explicitly in terms of observed data. We derive the asymptotic distributions of our estimators. Moreover, the results of Monte Carlo experiments show that the proposed estimators perform well in finite samples.     

Threshold estimation of Markov models with jumps and interest rate modeling

We reconstruct the level-dependent diffusion coefficient of a univariate semimartingale with jumps which is observed discretely. The consistency and asymptotic normality of our estimator are provided in the presence of both finite and infinite activity (finite variation) jumps. Our results rely on kernel estimation, using the properties of the local time of the data generating process, and the fact that it is possible to disentangle the discontinuous part of the state variable through those squared increments between observations not exceeding a suitable threshold function. We also reconstruct the drift and the jump intensity coefficients when they are level-dependent and jumps have finite activity, through consistent and asymptotically normal estimators. Simulated experiments show that the newly proposed estimators perform better in finite samples than alternative estimators, and this allows us to reexamine the estimation of a univariate model for the short term interest rate, for which we find fewer jumps and more variance due to the diffusion part than previous studies.     

Estimation of population variance in successive sampling

This paper proposes a class of estimators of finite population variance in successive sampling on two occasions and analyzes its properties. Isaki (J Am Stat Assoc 78:117?C123, 1983) motivated to consider the problem of estimation of finite population variance in survey sampling, and its extension to the case of successive sampling is much interesting, and the theory developed here will be helpful to those involved in such analysis in future. To our knowledge this is the first attempt made by the authors in this direction. An empirical study based on real populations and moderate sample sizes demonstrates the usefulness of the proposed methodology. In addition, this paper also presents a through review on successive sampling.     

A regularization approach to the many instruments problem

This paper focuses on the estimation of a finite dimensional parameter in a linear model where the number of instruments is very large or infinite. In order to improve the small sample properties of standard instrumental variable (IV) estimators, we propose three modified IV estimators based on three different ways of inverting the covariance matrix of the instruments. These inverses involve a regularization or smoothing parameter. It should be stressed that no restriction on the number of instruments is needed and that all the instruments are used in the estimation. We show that the three estimators are asymptotically normal and attain the semiparametric efficiency bound. Higher-order analysis of the MSE reveals that the bias of the modified estimators does not depend on the number of instruments. Finally, we suggest a data-driven method for selecting the regularization parameter. Interestingly, our regularization techniques lead to a consistent nonparametric estimation of the optimal instrument.