随机效应空间滞后单指数面板模型

对随机效应空间滞后单指数面板模型,本文构建了该模型的截面极大似然估计方法,从理论证明和数值模拟两方面分别考察了其估计量的大样本性质和小样本表现。研究结果表明:(1)在大样本条件下,估计量均具有一致性,并且参数估计量具有渐近正态性。(2)在小样本条件下,各估计量依然具有良好的表现,其精度随着样本容量的增加而提高;空间权重矩阵结构的复杂性对空间相关系数的估计量影响较大,但对其他估计量的影响较小。     

一种新的空间权重矩阵选择方法

空间权重矩阵选择问题一直是空间计量经济学中的一个难题,权重矩阵的选择正确与否关系到模型的最终估计结果。本文在空间滞后模型框架下,把空间权重矩阵选择问题转化为变量选择问题,然后利用CWB方法进行变量选择。中国城市服务业集聚机理实证研究显示,利用本文所提出的方法所选取的空间权重矩阵较为合理,进而可以减少因为空间权重矩阵误设问题而引起的模型估计偏误。在大样本情形下,该方法可以非常有效地降低计算成本。     

内生初始假定下动态空间固定效应模型的拟极大似然估计

 本文提出了基于初始值为内生确定下的动态空间固定效应模型,综合考虑了可直接观测和不可直接观测或无法观测的空间效应;推导了模型参数拟极大似然估计量具有的渐近性质及其渐近分布。对参数估计量性质的模拟检验结果表明,似然估计量的渐近性质随着样本容量的增加而改善,且其改善程度对时间维度变化较对空间维度变化更为敏感,在空间单元限定情形下有效增加时间维度可以显著改善估计量性质。中国省域经济收敛性的实证案例分析结果显示,本文构建的综合考虑双重空间结构的空间计量模型具有适用性和合理性。     

空间回归模型估计中的最小二乘法

空间回归模型由于引入了空间地理信息而使得其参数估计变得复杂,因为主要采用最大似然法,致使一般人认为在空间回归模型参数估计中不存在最小二乘法。通过分析空间回归模型的参数估计技术,研究发现,最小二乘法和最大似然法分别用于估计空间回归模型的不同的参数,只有将两者结合起来才能快速有效地完成全部的参数估计。数理论证结果表明,空间回归模型参数最小二乘估计量是最佳线性无偏估计量。空间回归模型的回归参数可以在估计量为正态性的条件下而实施显著性检验,而空间效应参数则不可以用此方法进行检验。     

时变系数空间自回归面板数据模型的极大似然估计

本文对扰动项存在跨时期的异方差、但不存在序列相关的时变系数空间自回归模型提出了极大似然的估计方法,并证明了该估计量的一致性,同时,证明了该估计量渐进服从正态分布,由此说明该估计量具有优良的大样本性质。同时,我们还对本文所提出估计量的小样本性质进行了数值模拟。本文研究表明,估计量虽然在N较小时偏差较大,但是随着N的不断增加,估计量偏差减小,体现了比较优良的渐进性质。同时,估计量的偏差会随着时期数的增加而变大,这说明本文所提出的估计方法适用于个体数较多、时期数较少的短面板数据。     

平滑转移空间随机前沿模型的贝叶斯估计

经济数据常存在空间相关性,忽略空间相关性会引发内生性问题,导致相应估计量有偏且不一致。空间随机前沿模型在随机前沿模型的基础上考虑了生产单元的空间相关性,更利于效率测算。然而现有空间随机前沿模型的生产函数形式单一,适用性较差,实证分析存在局限性。文章在空间随机前沿模型中引入平滑转移效应,构建了平滑转移空间随机前沿模型,该模型同时考虑了空间相关性和个体异质性,适用性较佳。为丰富估计方法,同时采用极大似然方法和贝叶斯方法估计模型,其中极大似然估计的核心在于推导对数似然函数、对数似然函数的最优化以及使用JLMS法估计技术效率,贝叶斯估计的核心在于推导未知参数的后验分布及执行MCMC抽样。数值模拟结果显示：（1）极大似然估计和贝叶斯估计的估计精度均较高,其中贝叶斯估计的估计精度略高于极大似然估计;增加样本容量,贝叶斯估计和极大似然估计的估计精度更高。（2）若忽略空间效应或者平滑转移效应,则估计精度较低。     

基于半参数方法的模型辅助抽样估计研究

在总结现有模型辅助估计方法的基础上,本文通过构造一种半参数超总体模型,同时结合广义差分估计思想提出一种新型的模型辅助估计量。该估计量比传统的非参数和半参数回归估计利用更少、更易得到的辅助信息,即只需利用和广义回归估计相同的辅助信息,但一般会比广义回归估计拥有更高的估计精度。理论证明了该估计量是渐近设计无偏和设计一致的,其渐近设计均方误差为广义差分估计量的方差。模拟结果显示：其至少与广义回归估计一样好;对于线性程度越低的超总体模型,其估计精度比广义回归估计有越明显的提高;就本文模拟而言,光滑参数在0.04～0.12间适当取值时其会取到相对较好的估计效果。     

样本选择模型的一个简单半参数估计量

本文发展了一个针对样本选择模型的两阶段半参数估计量,其首先在第一阶段基于对数欧几里得分布差异测度估计离散选择概率,进而在第二阶段利用非参数sieve方法估计一个包含参数和非参数部分的部分线性模型以得到模型参数的估计。相对于文献中已有的半参数估计量,该估计量的计算更加简便,且计算负担相对较小。我们说明了该半参数估计量的一致性和渐近正态性,同时给出了其渐近方差的计算公式。蒙特卡洛模拟结果符合我们的理论结论。     

基于空间模型的小域估计方法

小域估计成为当今抽样调查的热点问题之一，日益受到社会各界的关注。小域估计多采用基于模型的估计方法，其中以线性混合模型最为普遍，这种模型通常假定域随机效应是独立的。但是，在实际各个域之间往往表现出一定的空间相关性，并且这种相关性随着距离的增加而减小，若忽视这种空间效应，估计的精度会大大的降低。本文运用域随机效应为空间相关的空间模型来解决空间数据下的小域估计问题，并用基于这种空间模型的权数的方法得到了目标变量的稳健估计量，很大程度上提高小域估计的精度，是一种比较好的小域估计方法。     

空间门限随机前沿模型的构建与估计

基于空间计量视角拓展门限随机前沿模型,从技术效率时变和非时变两个层面分别构建空间门限随机前沿模型。模型同时考虑了生产单元的异质性和空间相关性,适用性较佳。分别使用两阶段最小二乘法和极大似然方法估计非时变和时变层面下的参数,使用JLMS法估计效率。蒙特卡罗结果表明:此方法的估计精度较高。随着样本容量的增加,估计精度增加;忽略空间效应或者门限效应,估计精度较低。     

k-Step shape estimators based on spatial signs and ranks

In this paper, the shape matrix estimators based on spatial sign and rank vectors are considered. The estimators considered here are slight modifications of the estimators introduced in Dümbgen (1998) and Oja and Randles (2004) and further studied for example in Sirkiä et al. (2009). The shape estimators are computed using pairwise differences of the observed data, therefore there is no need to estimate the location center of the data. When the estimator is based on signs, the use of differences also implies that the estimators have the so called independence property if the estimator, that is used as an initial estimator, has it. The influence functions and limiting distributions of the estimators are derived at the multivariate elliptical case. The estimators are shown to be highly efficient in the multinormal case, and for heavy-tailed distributions they outperform the shape estimator based on sample covariance matrix.     

On the efficiency property of the linear estimators in the bivariate pearson type vii distribution

In this paper the efficiency property of the estimators of the parameters of the bivariate Pearson type VII distribution is studied inside the family of linear estimators, assuming that the sample is constituted by dependent random vectors. It is proven that, although there are not efficient linear estimators, the sample mean and the sample covariance matrix (affected by an unbiasedness weighting) are unbiased linear estimators of minimum distance to the Cramér-Rao lower bound. Finally, a numerical simulation example shows that the proposed estimators are computationally feasible.     

Sequential shrinkage estimation of independent normal means with unkown variances

The Paper considers estimation of the p(> 3)-variate normal mean when the variance-covariance matrix is diagonal with unknown diagonal elements. A class of James-Stein estimators is developed, and is compared with the sample mean under an empirical minimax stopping rule. Asymptotic risk expansions are provided for both the sequential sample mean and the sequential James-Stein estimators. It is shown that the James-Stein estimators dominate the sample mean in a certain asymptotic sense.     

Estimation of the optimal portfolio weights by shrinking the mean vector towards a linear subspace

To obtain estimators of mean-variance optimal portfolio weights, Stein-type estimators of the mean vector that shrink a sample mean towards the grand mean have been applied. However, the dominance of these estimators has not been shown under the loss function used in the estimation problem of the mean-variance optimal portfolio weights, which is different than the quadratic function for the case in which the covariance matrix is unknown. We analytically give the conditions for Stein-type estimators that shrink towards the grand mean, or more generally, towards a linear subspace, to improve upon the classical estimators, which are obtained by simply plugging in sample estimates. We also show the dominance when there are linear constraints on portfolio weights.     

Conditional tests for elliptical symmetry using robust estimators

This paper presents a procedure for testing the hypothesis that the underlying distribution of the data is elliptical when using robust location and scatter estimators instead of the sample mean and covariance matrix. Under mild assumptions that include elliptical distributions without first moments, we derive the test statistic asymptotic behavior under the null hypothesis and under special alternatives. Numerical experiments allow to compare the behavior of the tests based on the sample mean and covariance matrix with that based on robust estimators, under various elliptical distributions and different alternatives. We also provide a numerical comparison with other competing tests.     

Selection of Variables in Multivariate Regression Models for Large Dimensions

The Akaike information criterion, AIC, and Mallows’ C _p statistic have been proposed for selecting a smaller number of regressors in the multivariate regression models with fully unknown covariance matrix. All of these criteria are, however, based on the implicit assumption that the sample size is substantially larger than the dimension of the covariance matrix. To obtain a stable estimator of the covariance matrix, it is required that the dimension of the covariance matrix is much smaller than the sample size. When the dimension is close to the sample size, it is necessary to use ridge-type estimators for the covariance matrix. In this article, we use a ridge-type estimators for the covariance matrix and obtain the modified AIC and modified C _p statistic under the asymptotic theory that both the sample size and the dimension go to infinity. It is numerically shown that these modified procedures perform very well in the sense of selecting the true model in large dimensional cases.     

GMM estimation in partial linear models with endogenous covariates causing an over-identified problem

ABSTRACT

We study partial linear models where the linear covariates are endogenous and cause an over-identified problem. We propose combining the profile principle with local linear approximation and the generalized moment methods (GMM) to estimate the parameters of interest. We show that the profiled GMM estimators are root? n consistent and asymptotically normally distributed. By appropriately choosing the weight matrix, the estimators can attain the efficiency bound. We further consider variable selection by using the moment restrictions imposed on endogenous variables when the dimension of the covariates may be diverging with the sample size, and propose a penalized GMM procedure, which is shown to have the sparsity property. We establish asymptotic normality of the resulting estimators of the nonzero parameters. Simulation studies have been presented to assess the finite-sample performance of the proposed procedure.     

A BLUP Synthetic Versus an EBLUP Estimator: An Empirical Study of a Small Area Estimation Problem

Model-based estimators are becoming very popular in statistical offices because Governments require accurate estimates for small domains that were not planned when the study was designed, as their inclusion would have produced an increase in the cost of the study. The sample sizes in these domains are very small or even zero; consequently, traditional direct design-based estimators lead to unacceptably large standard errors. In this regard, model-based estimators that 'borrow information' from related areas by using auxiliary information are appropriate. This paper reviews, under the model-based approach, a BLUP synthetic and an EBLUP estimator. The goal is to obtain estimators of domain totals when there are several domains with very small sample sizes or without sampled units. We also provide detailed expressions of the mean squared error at different levels of aggregation. The results are illustrated with real data from the Basque Country Business Survey.