企业净产值价格模型及其应用

本文试图通过建立企业净产值价格模型来探讨企业工业净产值的计算方法,以及净产值价格模型在企业管理中的应用问题。价格模型的建立,解决了在工业净产值计算中统计核算与会计核算不一致的问题,并使之成为企业投入产出核算体系的一个重要组成部分。     

微观分析模拟模型及其应用简介

微观分析模拟模型(Microanlgtic Simulation Models)是以住户、个人、企业等微观元素为基本研究对象,以某项经济政策规定的具体条款和具体实施办法为控制条件而构造的一种经济数学模型。它利用计算机模拟的方法,模拟有关经济政策的实施过程,通过模拟预先给出政策实施后的宏观经济效果以及对微观元素影响程度的定量分析结果,由此分析和评价有关政策的可行性和科学性。     

带有阶跃函数的GM（1,1）模型（指数模型）参数估计的逐步逼近法

The paper provides a modeling method of the GM(1,1) model with step function, and also a gradual approximation method of parameter estimation.     

因子多元ARCH模型的因子选择及其应用

一、引言Ｋｒａｆｔ和Ｅｎｇｅｌ(1982 )在一元ＡＲＣＨ模型的基础上提出了多元ＡＲＣＨ模型 ,多元ＧＡＲＣＨ模型也伴随着一元ＧＡＲＣＨ模型的提出而出现。但多元ＡＲＣＨ或ＧＡＲＣＨ模型并没有同一元ＡＲＣＨ或ＧＡＲＣＨ模型一样在实际研究中 ,尤其是经济金融领域被广泛应用。其主要原因在于多元ＡＲＣＨ或ＧＡＲＣＨ模型中待估的未知参数过多。一方面 ,我们无法给出合理的如此多的未知参数的初值 ,以求的得它们的数值解 ;另一方面 ,即使得到了这么多的未知参数的估计值 ,也不可能全面考虑所有的拟合参数为投资决策提供理论依据。因此 …     

外资变量与宏观经济变量的相关分析

利用外资是中国对外开放的主要内容。经过１７年的摸索前进和不断发展,中国吸收外资进展很快。据联合国贸发会议发表的《世界投资报告》,中国吸收的外资已连续３年仅次于美国,保持世界第二。据统计,截至１９９６年６月,中国实际利用外资已达１５４６亿美元,开业的三...     

偏离-份额分析空间模型及其应用

 传统的偏离-份额分析模型没有考虑区域之间的空间交互作用,为此国外出现了空间拓展模型的探索、论证与应用,然而目前国内仍主要停留在对传统模型的应用。本文探讨了如何构建空间权重矩阵来表示区域间的空间相互作用,并介绍了国外较成熟的偏离-份额分析空间模型,以江苏为例应用传统模型和空间模型进行了实证对比分析。结果表明,江苏省第二、三产业在全国具有较强的竞争力,经济总量及各产业的增长中,竞争力因素比产业结构因素贡献更大;但相对周边邻近省份,江苏省各产业并没有表现出竞争优势;邻近省份对江苏经济增长产生了较大的正面影响,但江苏没有能够充分利用这种影响,因此带来了一定的理论经济损失。     

线性相关模型及其估计

线性相关模型及其估计雷钦礼本文针对经济和社会研究领域中相关变量及其观测数据的特点并吸收回归分析中模型描述分析的思想，提出相关模型描述分析方法，用于讨论线性相关模型及其估计方法。一、两变量线性相关模型与估计假设两个线性相关的随机变量为Ｘｆｌｌｙ，其样本...     

非均衡经济计量模型及其估计

非均衡经济计量模型是在经济计量理论和非均衡市场大量统计观察的相互作用下发展起来的。它的开创者是美国经济计量学家费阿和杰斐。 一、基本模型 假设需求方程和供给方程如下。需求方程 Dt== a0X_t~D μ_t~D (t=1,2,…,T)(1)供给方程 St=β_0X_t~sX_t~s μ_t~s (t=1,2,…,T)(2)这里,Dt表示时期t的需求量,St表示时期t的供给量,X_t~D、X_t~s是各种外生变量的向量,a0,β0是待估参数,μ_t~D,μ_t~s是随机误差项,并假设μ_t~D,μ_t~s的均值为0方差为常数,无序列相关、     

中国地区间投入产出模型的编制及其问题

一、前言投入产出技术作为一种现代数量经济分析方法自 2 0世纪 6 0年代初被引入中国以来 ,经过国内学者几十年的努力 ,取得了丰富的理论和实践成果。不但设计和开发了具有中国特色的投入产出表 ,表的编制也实现了系列化和制度化。绝大多数的省份都编制了自己的投入产出表 ,而且在理论和方法的创新上作出了自己的贡献。如投入占用产出模型 ,投入产出对称模型等等 ,但是实际应用中进展较慢的是地区间投入产出模型的编制和应用 ,除了区域性的比较成功的苏南苏北 ,疆南疆北投入产出模型外 ,至今尚未编制出一套全国范围内地区间的投入产出表。近…     

回归分析中虚拟变量的系数转换

回归分析中虚拟变量的系数转换曹志祥在工业统计回归分析中,有时遇到自变量是属性变量的情形,即该变量描述的现象是足性的,或是可分类的,但回归分析中不宜直接使用属性变量。因为,对属性变量所赋与的离散值之间的相等间距掩盖了不同属类之间的差异,用属性变量直接回...     

Bayesian Variable Selection in Markov Mixture Models

Monte Carlo simulation is used to evaluate the actual confidence levels of five different approximations for confidence intervals for the probability of success in Markov dependent trials. The approximations involve the conditional probability of success as a nuisance parameter, and the effects of substituting Klotz's (1973), Price's (1976), and a new estimator are also evaluated. The new estimator is less biased and tends to increase the confidence level. A program for calculating the estimator and the confidence interval approximations is available.     

联合广义线性模型中的变量选择

在联合广义线性模型中,散度参数与均值都被赋予了广义线性模型的结构,本文主要考虑在只有分布的一阶矩和二阶矩指定的条件下,联合广义线性模型中均值部分的变量选择问题。本文采用广义拟似然函数,提出了新的模型选择准则(EAIC);该准则是Akaike信息准则的推广。论文通过模拟研究验证了该准则的效果。     

Robust Variable Selection in Linear Mixed Models

In this article, we develop a robust variable selection procedure jointly for fixed and random effects in linear mixed models for longitudinal data. We propose a penalized robust estimator for both the regression coefficients and the variance of random effects based on a re-parametrization of the linear mixed models. Under some regularity conditions, we show the oracle properties of the proposed robust variable selection method. Simulation study shows the robustness of the proposed method against outliers. In the end, the proposed methods is illustrated in the analysis of a real data set.     

Input Variable Selection in Neural Network Models

One of the most important issues in using neural networks for the analysis of real-world problems is the input variable selection problem. This article connects input variable selection with multiple testing in the neural network regression models. In the proposed procedure, the number and the type of input neurons are selected by means of a testing scheme, based on appropriate measures of relevance of a given input variable to the model. In order to avoid the data snooping problem, family-wise error rate is controlled by using the StepM method proposed by Romano and Wolf (2005 Romano , J. P. , Wolf , M. ( 2005 ). Exact and approximate stepdown methods for multiple hypothesis testing . J. Amer. Statist. Assoc. 100 : 94 – 108 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). The testing procedure is calibrated by using the subsampling, which is shown to deliver consistent results under weak assumptions on the data generating process and on the structure of the neural network model.     

Hierarchical Variable Selection in Polynomial Regression Models

Significance tests on coefficients of lower-order terms in polynomial regression models are affected by linear transformations. For this reason, a polynomial regression model that excludes hierarchically inferior predictors (i.e., lower-order terms) is considered to be not well formulated. Existing variable-selection algorithms do not take into account the hierarchy of predictors and often select as “best” a model that is not hierarchically well formulated. This article proposes a theory of the hierarchical ordering of the predictors of an arbitrary polynomial regression model in m variables, where m is any arbitrary positive integer. Ways of modifying existing algorithms to restrict their search to well-formulated models are suggested. An algorithm that generates all possible well-formulated models is presented.     

Latent Variable Models for Mixed Discrete and Continuous Outcomes

We propose a latent variable model for mixed discrete and continuous outcomes. The model accommodates any mixture of outcomes from an exponential family and allows for arbitrary covariate effects, as well as direct modelling of covariates on the latent variable. An EM algorithm is proposed for parameter estimation and estimates of the latent variables are produced as a by-product of the analysis. A generalized likelihood ratio test can be used to test the significance of covariates affecting the latent outcomes. This method is applied to birth defects data, where the outcomes of interest are continuous measures of size and binary indicators of minor physical anomalies. Infants who were exposed in utero to anticonvulsant medications are compared with controls.     

Two-Stage Bayesian Model Averaging in Endogenous Variable Models

Economic modeling in the presence of endogeneity is subject to model uncertainty at both the instrument and covariate level. We propose a Two-Stage Bayesian Model Averaging (2SBMA) methodology that extends the Two-Stage Least Squares (2SLS) estimator. By constructing a Two-Stage Unit Information Prior in the endogenous variable model, we are able to efficiently combine established methods for addressing model uncertainty in regression models with the classic technique of 2SLS. To assess the validity of instruments in the 2SBMA context, we develop Bayesian tests of the identification restriction that are based on model averaged posterior predictive p-values. A simulation study showed that 2SBMA has the ability to recover structure in both the instrument and covariate set, and substantially improves the sharpness of resulting coefficient estimates in comparison to 2SLS using the full specification in an automatic fashion. Due to the increased parsimony of the 2SBMA estimate, the Bayesian Sargan test had a power of 50% in detecting a violation of the exogeneity assumption, while the method based on 2SLS using the full specification had negligible power. We apply our approach to the problem of development accounting, and find support not only for institutions, but also for geography and integration as development determinants, once both model uncertainty and endogeneity have been jointly addressed.     

贝叶斯非线性混合效应模型及其应用研究

由于常用的线性混合效应模型对具有非线性关系的纵向数据建模具有一定的局限性,因此对线性混合效应模型进行扩展,根据变量间的非线性关系建立不同的非线性混合效应模型,并根据因变量的分布特征建立混合分布模型。基于一组实际的保险损失数据,建立多项式混合效应模型、截断多项式混合效应模型和B样条混合效应模型。研究结果表明,非线性混合效应模型能够显著改进对保险损失数据的建模效果,对非寿险费率厘定具有重要参考价值。     

Two Applications of Statistics to Traffic Models

Two statistical applications for estimation and prediction of flows in traffic networks are presented. In the first, the number of route users are assumed to be independent α-shifted gamma Γ(θ, λ₀) random variables denoted H(α, θ, λ₀), with common λ₀. As a consequence, the link, OD (origin-destination) and node flows are also H(α, θ, λ₀) variables. We assume that the main source of information is plate scanning, which permits us to identify, totally or partially, the vehicle route, OD and link flows by scanning their corresponding plate numbers at an adequately selected subset of links. A Bayesian approach using conjugate families is proposed that allows us to estimate different traffic flows. In the second application, a stochastic demand dynamic traffic model to predict some traffic variables and their time evolution in real networks is presented. The Bayesian network model considers that the variables are generalized Beta variables such that when marginally transformed to standard normal become multivariate normal. The model is able to provide a point estimate, a confidence interval or the density of the variable being predicted. Finally, the models are illustrated by their application to the Nguyen Dupuis network and the Vermont-State example. The resulting traffic predictions seem to be promising for real traffic networks and can be done in real time.     

New Robust Variable Selection Methods for Linear Regression Models

Motivated by an entropy inequality, we propose for the first time a penalized profile likelihood method for simultaneously selecting significant variables and estimating unknown coefficients in multiple linear regression models in this article. The new method is robust to outliers or errors with heavy tails and works well even for error with infinite variance. Our proposed approach outperforms the adaptive lasso in both theory and practice. It is observed from the simulation studies that (i) the new approach possesses higher probability of correctly selecting the exact model than the least absolute deviation lasso and the adaptively penalized composite quantile regression approach and (ii) exact model selection via our proposed approach is robust regardless of the error distribution. An application to a real dataset is also provided.