非经典计量经济建模方法论的特征分析与比较研究

 按照内容的沿革,计量经济模型分为经典计量经济模型和非经典计量经济模型。非经典计量经济建模方法论既是经典计量经济建模方法论的发展和延伸,又在建模理念、建模方法、模型应用等方面有着很大的不同。本文从数据类型、模型变量、建模对象、参数形式、建模思想等几个方面,对非经典计量经济建模方法论进行了系统的梳理、归纳和分析,重点比较了其与经典计量经济建模方法论的不同特征,对计量经济模型方法论发展演进的规律进行了初步的总结。     

经济统计学与计量经济学等相关学科的关系及发展前景

本文从统计学和经济学统一的视角,分析与阐述经济统计学与计量经济学等相关学科——概率论、数理统计学、计量经济学以及经济理论（包括数理经济学）之间的相互关系及发展前景。作为从样本信息推断母体特征的一般方法论,数理统计学由于符合人类科学研究的过程与需要,因而在自然科学和社会科学的很多领域得到了广泛应用。计量经济学是经济实证研究的推断方法论。经济统计学与计量经济学一起,构成经济实证研究完整的方法论,其中,作为经济测度方法论,经济统计学不仅提供定量描述经济实际运行的理论、方法与工具,它也是经济实证研究的先决条件与基础,是计量经济学理论发展的一个重要推动力量。经济统计学面临不少挑战,但有深厚的学科根基与巨大的发展空间,其作用是任何相关学科均不能替代的。统计学各个分支的交叉融合,将推动经济统计学和计量经济学的共同发展,从而进一步提升中国经济学实证研究的水平与科学性。     

计量经济学和统计学视角下的线性回归模型——再议线性回归模型的设定

从属性、构建方法及意义等方面,分析研究线性回归模型在计量经济学和统计学两学科视角下的差异,并根据这种差异进一步提出回归模型的基本设定思路。研究表明:识别这种差异是完成模型设定工作的基础性和必要性举措,有助于实现线性回归模型的正确设定。以经典例证对计量经济学和统计学回归模型在应用中的区别以及模型设定问题进行进一步展示和分析。     

企业组织文化对组织认同影响的实证分析

文章运用带有高阶潜变量的结构方程模型进行实证分析,得到了组织文化因素对组织认同的影响模型,拟合结果比较满意,是理想模型,结果显示:对组织认同影响最大的潜变量是效益取向.     

潜变量量尺的拓展及研究展望

随着潜类别分析(LCA)技术的发展,研究者对于总体异质性的问题越来越关注。在潜变量量尺拓展之后,潜类别模型的方法也被广泛的应用到各领域。在横断研究中,LCA与混合因素分析模型(FMA)常被使用在探索总体分群和因素分群的研究中;在追踪研究中,潜类别转换分析(LTA)重点讨论群的调节作用和分类结果随时间变化的不同,而潜类别增长分析(LCGA)和混合增长模型(GMM)则关注发展趋势;多水平模型(MLM)也针对嵌套数据应用在横断与追踪研究中,衍生出近年来成为热点的多水平混合模型(MMM)。     

基于拓展Jones模型的国内上市企业盈余能力分析

为了深入分析与研究中国国内上市企业盈余能力,利用统计学、计量经济学等的分析方法,通过构建拓展盈余管理经典模型---Jones模型,将选定企业的盈余相关数据有序地引入到拓展的Jones模型中分析研究结果,利用K-means聚类分析方法,分析中国国内上市企业发展中存在的优势及不足,从而为进一步提升国内上市企业的盈余能力提出了具体的思路和建设性意见。     

贝叶斯计量经济学及面板数据中的贝叶斯推断

随着贝叶斯理论的发展和计算机模拟等数值计算技术的提高,贝叶斯计量经济学开始迅速发展起来。文章在经典学派与贝叶斯学派比较中,简要回顾了贝叶斯计量经济学发展历程;并对面板数据中的贝叶斯方法的应用进行了举例。     

“计量模型多样性悖论”研究

在排除模型误用以后,将经济学研究中针对同一问题的研究,不同计量模型的结果各不相同,甚至结论截然相反问题称为"计量模型多样性悖论"。分析此问题产生的原因,主要是关键变量的多样性、方程形式设定的多样性和计量模型的多样性,提出采用元分析来解决这个问题,指出在元分析应用过程中应力求单个模型的精确,尽可能多地采用合适的模型,注意同类性质的结果才能进行元分析。同时,讨论元分析带来的新问题,比如对研究团队的计量经济学水平要求较高,增加了成本,延长了研究的时间,也加大了论文的篇幅等,但这是计量经济学发展过程中的正常现象,并不涉及采用元分析解决"计量模型多样性悖论"问题的科学性。     

对外贸易影响中国经济增长的动态分析

运用计量经济学方法对中国1980-2007年相关经济变量时间序列数据的平稳性进行ADF检验,对修正后表现平稳的变量进行协整分析,研究在长期内是否存在稳定的均衡关系。通过构建协整方程、误差修正模型和Granger因果关系式分析各经济变量在长期均衡和短期波动中相互因果关系的影响。研究结果表明：在短期内经济增长更多受制于需求,而长期内取决于生产效率的提高。因此在继续重视出口贸易创造市场作用的前提下,要充分发挥外贸在增加要素供给和创造市场需求两个方面的作用。     

论经典统计财务困境预测模型的理论误区

文章对企业财务困境预测研究领域中的统计类判别模型进行了综述;分析了经典统计类财务困境预测模型的特点;从财务困境的界定、数据的非稳定性、非随机取样、财务困境的动态性、变量的选择等五个方面对经典统计类财务困境预测模型存在的一些理论误区进行了系统深入地剖析和理论评价;对今后企业财务困境预测研究的方向和趋势进行了展望。     

Multistage sampling for latent variable models

I consider the design of multistage sampling schemes for epidemiologic studies involving latent variable models, with surrogate measurements of the latent variables on a subset of subjects. Such models arise in various situations: when detailed exposure measurements are combined with variables that can be used to assign exposures to unmeasured subjects; when biomarkers are obtained to assess an unobserved pathophysiologic process; or when additional information is to be obtained on confounding or modifying variables. In such situations, it may be possible to stratify the subsample on data available for all subjects in the main study, such as outcomes, exposure predictors, or geographic locations. Three circumstances where analytic calculations of the optimal design are possible are considered: (i) when all variables are binary; (ii) when all are normally distributed; and (iii) when the latent variable and its measurement are normally distributed, but the outcome is binary. In each of these cases, it is often possible to considerably improve the cost efficiency of the design by appropriate selection of the sampling fractions. More complex situations arise when the data are spatially distributed: the spatial correlation can be exploited to improve exposure assignment for unmeasured locations using available measurements on neighboring locations; some approaches for informative selection of the measurement sample using location and/or exposure predictor data are considered.     

Latent Variable Modelling: A Survey*

Abstract. Latent variable modelling has gradually become an integral part of mainstream statistics and is currently used for a multitude of applications in different subject areas. Examples of ‘traditional’ latent variable models include latent class models, item–response models, common factor models, structural equation models, mixed or random effects models and covariate measurement error models. Although latent variables have widely different interpretations in different settings, the models have a very similar mathematical structure. This has been the impetus for the formulation of general modelling frameworks which accommodate a wide range of models. Recent developments include multilevel structural equation models with both continuous and discrete latent variables, multiprocess models and nonlinear latent variable models.     

Latent root regression: a biased regression methodology for use with collinear predictor variables

Many different biased regression techniques have been proposed for estimating parameters of a multiple linear regression model when the predictor variables are collinear. One particular alternative, latent root regression analysis, is a technique based on analyzing the latent roots and latent vectors of the correlation matrix of both the response and the predictor variables. It is the purpose of this paper to review the latent root regression estimator and to re-examine some of its properties and applications. It is shown that the latent root estimator is a member of a wider class of estimators for linear models     

Selection of Latent Variables for Multiple Mixed‐outcome Models

Latent variable models have been widely used for modelling the dependence structure of multiple outcomes data. However, the formulation of a latent variable model is often unknown a priori, the misspecification will distort the dependence structure and lead to unreliable model inference. Moreover, multiple outcomes with varying types present enormous analytical challenges. In this paper, we present a class of general latent variable models that can accommodate mixed types of outcomes. We propose a novel selection approach that simultaneously selects latent variables and estimates parameters. We show that the proposed estimator is consistent, asymptotically normal and has the oracle property. The practical utility of the methods is confirmed via simulations as well as an application to the analysis of the World Values Survey, a global research project that explores peoples’ values and beliefs and the social and personal characteristics that might influence them.     

Semiparametric transformation models with Bayesian P-splines

In this paper, we aim to develop a semiparametric transformation model. Nonparametric transformation functions are modeled with Bayesian P-splines. The transformed variables can be fitted to a general nonlinear mixed model, including linear or nonlinear regression models, mixed effect models, factor analysis models, and other latent variable models as special cases. Markov chain Monte Carlo algorithms are implemented to estimate transformation functions and unknown quantities in the model. The performance of the developed methodology is demonstrated with a simulation study. Its application to a real study on polydrug use is presented.     

网络文化对高校校园文化的影响——基于结构方程模型的分析

针对目前高校网络迅速发展的现状,在大量问卷调查的基础上,利用结构方程模型分析了网络文化对高校校园文化的影响。选择网络文化与高校校园文化为潜变量,网络意识文化、网络制度文化、网络物质文化、校园精神文化、校园物质文化为可测变量。通过对各可测变量之间的关系进行统计分析,得出网络文化能够对高校校园文化产生重要影响的结论,为促进网络文化与校园文化的共同发展提供了理论依据。     

General location multivariate latent variable models for mixed correlated bounded continuous,ordinal, and nominal responses with non-ignorable missing data

Using a multivariate latent variable approach, this article proposes some new general models to analyze the correlated bounded continuous and categorical (nominal or/and ordinal) responses with and without non-ignorable missing values. First, we discuss regression methods for jointly analyzing continuous, nominal, and ordinal responses that we motivated by analyzing data from studies of toxicity development. Second, using the beta and Dirichlet distributions, we extend the models so that some bounded continuous responses are replaced for continuous responses. The joint distribution of the bounded continuous, nominal and ordinal variables is decomposed into a marginal multinomial distribution for the nominal variable and a conditional multivariate joint distribution for the bounded continuous and ordinal variables given the nominal variable. We estimate the regression parameters under the new general location models using the maximum-likelihood method. Sensitivity analysis is also performed to study the influence of small perturbations of the parameters of the missing mechanisms of the model on the maximal normal curvature. The proposed models are applied to two data sets: BMI, Steatosis and Osteoporosis data and Tehran household expenditure budgets.     

A mixture latent variable model for modeling mixed data in heterogeneous populations and its applications

Latent variable models are widely used for jointly modeling of mixed data including nominal, ordinal, count and continuous data. In this paper, we consider a latent variable model for jointly modeling relationships between mixed binary, count and continuous variables with some observed covariates. We assume that, given a latent variable, mixed variables of interest are independent and count and continuous variables have Poisson distribution and normal distribution, respectively. As such data may be extracted from different subpopulations, consideration of an unobserved heterogeneity has to be taken into account. A mixture distribution is considered (for the distribution of the latent variable) which accounts the heterogeneity. The generalized EM algorithm which uses the Newton–Raphson algorithm inside the EM algorithm is used to compute the maximum likelihood estimates of parameters. The standard errors of the maximum likelihood estimates are computed by using the supplemented EM algorithm. Analysis of the primary biliary cirrhosis data is presented as an application of the proposed model.     

Generalized Linear Factor Models: A New Local EM Estimation Algorithm

In this article, a general approach to latent variable models based on an underlying generalized linear model (GLM) with factor analysis observation process is introduced. We call these models Generalized Linear Factor Models (GLFM). The observations are produced from a general model framework that involves observed and latent variables that are assumed to be distributed in the exponential family. More specifically, we concentrate on situations where the observed variables are both discretely measured (e.g., binomial, Poisson) and continuously distributed (e.g., gamma). The common latent factors are assumed to be independent with a standard multivariate normal distribution. Practical details of training such models with a new local expectation-maximization (EM) algorithm, which can be considered as a generalized EM-type algorithm, are also discussed. In conjunction with an approximated version of the Fisher score algorithm (FSA), we show how to calculate maximum likelihood estimates of the model parameters, and to yield inferences about the unobservable path of the common factors. The methodology is illustrated by an extensive Monte Carlo simulation study and the results show promising performance.     

Evaluating surrogate variables for improving microarray multiple testing inference

The use of surrogate variables has been proposed as a means to capture, for a given observed set of data, sources driving the dependency structure among high-dimensional sets of features and remove the effects of those sources and their potential negative impact on simultaneous inference. In this article we illustrate the potential effects of latent variables on testing dependence and the resulting impact on multiple inference, we briefly review the method of surrogate variable analysis proposed by Leek and Storey (PNAS 2008; 105:18718-18723), and assess that method via simulations intended to mimic the complexity of feature dependence observed in real-world microarray data. The method is also assessed via application to a recent Merck microarray data set. Both simulation and case study results indicate that surrogate variable analysis can offer a viable strategy for tackling the multiple testing dependence problem when the features follow a potentially complex correlation structure, yielding improvements in the variability of false positive rates and increases in power.