计量模型中的加总偏误识别与检验

采用逐步分类加总方法,运用美加与欧盟五国大豆贸易经验数据,依据加总的一般形式对欧盟转基因政策贸易效应研究中的加总偏误问题进行分路径、分步骤的识别与检验,考察加总偏误的存在性及偏误来源,有效诊断加总过程内生性存在的路径与节点。根据有限剔除法将部分内生性因素进行剔除,分析结果显示修正模型中的加总偏误得到显著改善。     

计量模型中的加总偏误与内生性：一种数值模拟方法

理论文献已经证明了细分数据模型的内生性和加总过程产生的内生性是加总偏误的根本原因。但是由于内生性涉及误差项与回归量之间的相关性问题,试图通过实证方法审视这两类内生性对加总偏误的影响变得比较困难,而数值模拟却是一个较为理想的方法。在数值模拟中,我们通过控制随机变量的分布形式以及随机变量之间的相关程度,进而对两类内生性因素产生的加总偏误进行全面细致的考察。本文的研究将为加总偏误的内生性解释提供有力证据。     

加总模型与扩展的高斯—马尔科夫定理

本文将经典的高斯—马尔科夫定理推广到加总数据层面,探讨三类加总形式下加总模型参数估计的最小方差线性无偏性质;揭示加总模型最小方差线性无偏性与非加总模型最小方差线性无偏性之间的关系。在满足正态误差项的假设下,进一步分析加总模型参数所具有的最优无偏特征。最后运用蒙特卡洛方法对加总模型的参数无偏性特征进行数值模拟。     

操纵性应计模型的一个改进——基于动态面板估计的实证分析

使用动态面板对操纵性应计模型进行估计可以克服截面数据模型的内生性估计偏误。采用1999—2011年中国A股市场相关数据,对动态面板模型和其他模型的检测能力进行比较,结果表明：在对第Ⅱ类错误进行检验时,动态面板模型检测能力显著优于其他模型;在对第1类错误、审计师意见检验时,动态面板模型检测能力与其他模型接近。     

中国城乡综合基尼系数测算的一种新改进

城乡综合基尼系数测算是在中国现有统计方式和数据可获得条件下的一个特殊问题,主要表现为如何在仅有城镇和农村各自的分组收入数据约束下更好地估算城乡综合基尼系数（或称城乡合一基尼系数、全国基尼系数）。针对包括城乡加权法和分层加权法等在内的子群分解法无法解决的城乡收入数据重叠难题,函数加总法通过城乡收入分布或密度函数加总形式予以化解,但既有的函数加总方法却又引发了对全国和组内最高收入的依赖问题。本文采取城乡间接洛伦兹曲线加总的形式规避了对两种最高收入的依赖,并以CHIPs2007数据为例对城乡综合基尼系数若干方法计算结果的有效性和稳健性进行了比较。此外,文章还在新改进方法的框架下进一步表达了基尼系数的城乡分解公式。     

中国股价行为模式分析

本文以西方成熟股市为基础的股价行为模型，对我国股价行为特性及模式展开讨论。分析表明，中国股市长期存在着随机投机冲击所引起的股价泡沫，而这种泡沫的内生性决定当期股票价格不能包含过去股票价格的所有信息，从而导致了股价行为的复合性特征，利用剔除法及加总原理推导出了我国股票价格的行为模型。     

迪维西亚(Divisia)货币总量VAR模型的预测分析

本文针对最新编制的我国迪维西亚(Divisia)货币总量(指数)数据,运用向量自回归(VAR)模型预测实际产出和物价指数并与简单加总货币总量所得到的结果进行比较.结果显示,在预测我国实际产出值的VAR模型中,含有Divisia狭义货币(M1)总量的模型比含有简单加总货币总量M1的模型更精确;在预测我国的物价指数值的VAR模型中,含有Divisia广义货币(M2)总量的模型比含有简单加总货币总量M1和M2的模型更精确.     

基于信息耗损与补偿的会计加总分析——《企业会计准则第30号——财务报表列报》评价

从信息论的角度看,会计实际上是一个数据加总与分解的过程,如果信息使用者的偏好不变,其加总数据必然导致信息耗损,分解数据则必然导致信息补偿。2006年初新颁布的《企业会计准则第30号——财务报表列报》对利润表的列报方式做了较大的变动。本文借鉴信息熵理论,构建了有别于传统的报表编制评价模型,并通过对2001~2005年某上市公司的利润表进行重述,实际考察了《企业会计准则第30号》对利润表的信息耗损与补偿,在此基础上给出了报表编制的一般性程序。     

混频Granger因果关系检验的功效和稳健性分析

研究目标：分析混频Granger因果关系检验的功效及其稳健性。研究方法：以货币与产出间的因果关系为例,比较分析不同情形下,混频和同低频Granger因果关系检验功效的差异性和检验结果的稳健性。研究发现：货币与产出间的因果关系不是一成不变的,混频Granger因果关系检验的水平和功效随着不同层次的货币供给、模型滞后阶数、预测期长度,变量的多寡,以及样本量的大小和所处的经济阶段的变化而呈现异质性和非对称性。研究创新：将混频技术与Granger因果关系结合起来,从不同角度全面系统地检验了货币与产出的因果关系,并分析了该方法的稳健性。研究价值：进行货币政策调控时,须评估不同情形下货币政策的作用效果,选取恰当的时机,采用更加合理的调控手段和方法。     

关于几个条件独立性检验统计量的构造与比较分析

变量间的条件独立性可视为在概率空间上对其因果关系的一种描述,因而可以通过检验变量之间的条件独立性来检验因果关系。文章详细介绍了几个条件独立性检验统计量的构造方法和基本原理,包括线性模型假设下的Fisher-z检验统计量和在非线性模型下或无法确定变量之间的模型时使用的3个非参数的条件独立性检验统计量,并对这几个不同的条件独立性检验统计量的检验效率进行了对比分析。     

Mean square error estimation in multi-stage sampling

Summary: Suppose for a homogeneous linear unbiased function of the sampled first stage unit (fsu)-values taken as an estimator of a survey population total, the sampling variance is expressed as a homogeneous quadratic function of the fsu-values. When the fsu-values are not ascertainable but unbiased estimators for them are separately available through sampling in later stages and substituted into the estimator, Raj (1968) gave a simple variance estimator formula for this multi-stage estimator of the population total. He requires that the variances of the estimated fsu-values in sampling at later stages and their unbiased estimators are available in certain `simple forms'. For the same set-up Rao (1975) derived an alternative variance estimator when the later stage sampling variances have more ‘complex forms’. Here we pursue with Raj's (1968) simple forms to derive a few alternative variance and mean square error estimators when the condition of homogeneity or unbiasedness in the original estimator of the total is relaxed and the variance of the original estimator is not expressed as a quadratic form.  We illustrate a particular three-stage sampling strategy and present a simulation-based numerical exercise showing the relative efficacies of two alternative variance estimators. Received: 19 February 1999     

Estimation from two-stage unequal probability sampling with missing units

A formula is presented for an unbiased estimator for the variance of an unbiased estimator of a survey population total as well as for an unbiased estimator of its variance based on sampling in two-stages following Rao et al. J Roy Stat Soc B 24: 482–491 (1962) scheme in both stages when the originally selected units in both stages cannot be fully covered in the survey but are to be randomly sub-sampled. The development is helpful to tackle non-responses if assumed to have occurred at random in either or both the stages     

Sampling Techniques for Big Data Analysis

In analysing big data for finite population inference, it is critical to adjust for the selection bias in the big data. In this paper, we propose two methods of reducing the selection bias associated with the big data sample. The first method uses a version of inverse sampling by incorporating auxiliary information from external sources, and the second one borrows the idea of data integration by combining the big data sample with an independent probability sample. Two simulation studies show that the proposed methods are unbiased and have better coverage rates than their alternatives. In addition, the proposed methods are easy to implement in practice.     

Stratification,midzuno-sen strategy and nonnegative unbiased variance estimation

When the ratio method is appropriate for estimating the population total one is faced with the problem of nonavailability of uniformly nonnegative unbiased variance estimators (nnuve). Here we highlight the twofold role of stratification in that it not only improves the efficiency of the ratio method of estimation but it also enhances the chances of getting uniformly nonnegative unbiased variance estimators.     

Estimation Optimality of Corrected AIC and Modified Cp in Linear Regression

Model selection criteria often arise by constructing unbiased or approximately unbiased estimators of measures known as expected overall discrepancies (Linhart & Zucchini, 1986, p. 19). Such measures quantify the disparity between the true model (i.e., the model which generated the observed data) and a fitted candidate model. For linear regression with normally distributed error terms, the "corrected" Akaike information criterion and the "modified" conceptual predictive statistic have been proposed as exactly unbiased estimators of their respective target discrepancies. We expand on previous work to additionally show that these criteria achieve minimum variance within the class of unbiased estimators.     

On the existence of locally mvu estimators

It is proved that there exists an unbiased estimator for some real parameter of a class of distributions, which has minimal variance for some fixed distribution among all corresponding unbiased estimators, if and. only if the corresponding minimal variances for all related unbiased estimation problems concerning finite subsets of the underlying family of distributions are bounded. As an application it is shown that there does not exist some unbiased estimator for θ^k+c(ε≥0) with minimal variance for θ =0 among all corresponding unbiased estimators on the base of k i.i.d. random variables with a Cauchy-distribution, where θ denotes some location parameter.     

Unbiased estimators of mean, variance and sensitivity level for quantitative characteristics in finite population sampling

This paper considers the problem of procuring reliable information on sensitive quantitative characteristics without exposing respondents’ identities. A generalized optional randomized response procedure is proposed, which enables us to estimate some unknown population parameters unbiasedly. In particular, conditions for the assurance of unbiased estimations of mean, variance and sensitivity level are studied. Efficiency comparisons are also carried out to study the performance of the proposed procedure.     

A Unified View of Signal Extraction, Benchmarking, Interpolation and Extrapolation of Time Series

Time series data are often subject to statistical adjustments needed to increase accuracy, replace missing values and/or facilitate data analysis. The most common adjustments made to original observations are signal extraction (e.g. smoothing), benchmarking, interpolation and extrapolation. In this article, we present a general dynamic stochastic regression model, from which most of these adjustments can be performed, and prove that the resulting generalized least square estimator is minimum variance linear unbiased. We extend current methods to include those cases where the signal follows a mixed model (deterministic and stochastic components) and the errors are autocorrelated and heteroscedastic.     

Tests with correct size when instruments can be arbitrarily weak

This paper applies classical exponential-family statistical theory to develop a unifying framework for testing structural parameters in the simultaneous equations model under the assumption of normal errors with known reduced-form variance matrix. The results can be divided into the limited-information and full-information categories. In the limited-information model, it is possible to characterize the entire class of similar tests in a model with only one endogenous explanatory variable. In the full-information framework, this paper proposes a family of similar tests for subsets of endogenous variables’ coefficients. For both limited- and full-information models, there exist power upper bounds for unbiased tests. When the model is just-identified, the Anderson–Rubin, score, and (pseudo) conditional likelihood ratio tests are optimal. When the model is over-identified, the (pseudo) conditional likelihood ratio test has power close to the power envelope when identification is strong.     

Simultaneous optimal estimation in linear mixed models

Simultaneous optimal estimation in linear mixed models is considered. A necessary and sufficient condition is presented for the least squares estimator of the fixed effects and the analysis of variance estimator of the variance components to be of uniformly minimum variance simultaneously in a general variance components model. That is, the matrix obtained by orthogonally projecting the covariance matrix onto the orthogonal complement space of the column space of the design matrix is symmetric, each eigenvalue of the matrix is a linear combinations of the variance components and the number of all distinct eigenvalues of the matrix is equal to the the number of the variance components. Under this condition, uniformly optimal unbiased tests and uniformly most accurate unbiased confidence intervals are constructed for the parameters of interest. A necessary and sufficient condition is also given for the equivalence of several common estimators of variance components. Two examples of their application are given.