PP回归方法在预测模型中的应用－用PP回归建立“积累”“消费”模型

本文利用ＰＰ回归方法,建立“积累”“消费”回归模型,并与最小二乘法拟合的线性回归模型相比较,说明用ＰＰ回归建立的模型预测精度较高．     

Logistic回归模型在信用风险分析中的应用

通过运行SPSS,建立L og istic回归信用评价模型(cred it eva luation m odel),用来对中国2000年106家上市公司进行两类模式分类,这两类模式是指按照公司的经营状况分为“差”和“正常”两个小组.对每一家上市公司,考虑其经营状况的4个主要财务指标:每股收益、每股净资产、净资产收益率和每股现金流量.仿真结果表明,L og istic回归信用评价模型对总体106个样本,判别准确率达到99.06%.此外,本文的研究结果还发现,当利用SPSS的D iscrim inan t给出的模型系数建立的线性判别分析模型和利用SPSS的M u ltinom ia lL og istic给出的模型参数建立的L og istic回归模型,L og istic回归模型的判别结果不如线性判别模型.但如果剔除不合格的样本,或是将样本数据规格化,则可以提高L og istic回归模型的分类准确率.     

基于主成分回归模型的经济增长因素分析

在经济增长因素分析中,常用多元回归分析方法,但有时建立的回归模型拟合效果不好或不合理。为此本文给出建立主成分回归分析的方法。本文对经济增长给出两种回归分析方法,即建立主成分线性回归模型,分析经济增长的边际效应,建立主成分非线性回归模型,分析经济增长的弹性效应,实例表明效果很好。     

广义幂级数环的PP性质

作为幂级数环的推广,Ｒｉｂｅｎｂｏｉｍ引入了广义幂级数环的概念．设Ｒ是有单位元的交换环,（Ｊ,≤）是严格全序半群．本文中我们证明了如下结果：（１）广义幂级数环 ［［Ｒｓ］］是ＰＰ－环当且仅当Ｒ是ＰＰ－环且Ｂ（Ｒ）的任意 Ｓ－可标子集Ｃ在Ｂ（Ｒ）中有最小上界;（２）如果对任意ｓ∈Ｓ都有０≤ｓ,则［［Ｒｓ,≤］］是弱ＰＰ－环当且仅当Ｒ是弱ＰＰ－环．我们还给出了一个例子说明交换的弱ＰＰ－环可以不是ＰＰ－环．     

带模糊回归参数的线性回归模型

本文讨论了数值输入模糊数输出的观测数据的线性最小二乘拟合问题，建立了数值空间到模糊数空间的带模糊回归参数的线性回归模型，证明了模型解的存在性和唯一性，并得到了解的表达式。本模型应用简便，具有实用价值。     

我国通货膨胀的非参数回归模型

本文首先讨论非参数回归模型的局部核权最小二乘估计 ,然后建立我国通货膨胀非参数回归模型 ,最后研究了反映出口与通货膨胀关系的弹性系数     

门限自回归滑动平均模型——TARMA

一、引言 近年来,时间序列的理论及应用得到了很大的发展,各种模型也应运而生。人们在实际应用中越来越感到现有的线性模型,如AR,ARMA模型等,难以很好地刻划复杂的物理现象。因此,对非线性模型的讨论越来越活跃,已经提出了一些非线性模型。但这些模型一般都较复杂,局限性强,建立模型很麻烦,难以推广。1977年,汤家豪提出的门限自回归模型,简称“TAR”(Threshold Autoregression),较好地克服了这些缺点。它的计算复杂性与一般的AR模型相当,且能刻划线性模型难以刻划的物理现象。本文就是基于这一思想,进一步发展了这一模型,提出了一种新的非线性模型——门限自回归滑动平均模型(TARMA)。     

分次PP环的一个结论

本文证明了Ｚ－型分次简约环(?)是ＰＰ环（Ｂａｅｒ环）的充要条件是Ｒ是分次ＰＰ环（分次Ｂａｅｒ环）．     

通用回归分析模型

建立了通用的回归分析模型,它包含了直线、指数线、抛物线、双曲线等众多模型形式,因而具有广泛代表性.分析了通用模型中的常数项对模型曲线形状的影响.结合实例用计算机程序给出了相关参数的数值解法.通用模型与数值解法相结合,突破了回归分析中预先确定简单函数曲线误差大和复杂曲线求解难度大之间的矛盾问题,大幅度提高了计算精度和速度.     

经济预测中的正交回归分析

本文介绍了一种新的线性模型参数回归分析方法即正交回归，并以建立经济模型为例，对正交回归和经典回归的结果进行了比较。     

Granger因果检验的非线性进展及应用研究

Granger因果检验是计量经济学的重要组成部分,也是现代经济、金融学分析的重要工具,近年来Granger因果检验在非线性检验方向有了较大进展。本文在线性Granger因果检验的基础上,阐述了Granger因果检验的非线性进展,重点总结了针对一阶矩的基于回归模型、非参函数和信息理论的三大类非线性方法以及针对二阶矩的基于残差交叉相关系数和多元条件方差模型下的两大类非线性方法,讨论了不同非线性Granger方法中数据要求、核心模型、建模关键以及模型优缺点,提出了Granger因果检验"线性-非线性"的整体框架和研究范式.通过模型分析和比较,本文可为因果检验的非线性理论和模型研究提供参考,并对因果检验在经济和金融领域的更广泛应用提供支持。     

黑龙江省总体经济预测——基于扩散指数模型

本文以Stock and Watson提出的"扩散指数"模型为基础,建立一个两步骤的"扩散指数"预测模型,并应用于黑龙江省经济增长率的预测。除了依循Stock and Watson的作法外,本文也将变量依其特性区分为商品市场变量与货币市场变量,再分别针对各市场变量估计其扩散指数,然后根据这些指数进行预测。实证结果显示,"扩散指数"预测模型具有相当好的预测绩效,也优于一般模型的预测,因此可以作为未来总体经济预测上的另一种选择。     

DEA模型在资金分配和管理中的应用

资金的合理使用,是经济活动中的一个非常重要的问题.利用DEA的理论、方法模型,探讨资金的使用效率、分配的合理性,以及最佳资金预算的确定方法.涉及的DEA模型结构属于非参数的最优化DEA模型,以及DEA平行网络结构.模型中所使用的生产可能集是可以评价是否呈现"拥挤"迹象的.     

Empirical likelihood inference for linear transformation models

Empirical likelihood inference is developed for censored survival data under the linear transformation models, which generalize Cox's [Regression models and life tables (with Discussion), J. Roy. Statist. Soc. Ser. B 34 (1972) 187-220] proportional hazards model. We show that the limiting distribution of the empirical likelihood ratio is a weighted sum of standard chi-squared distribution. Empirical likelihood ratio tests for the regression parameters with and without covariate adjustments are also derived. Simulation studies suggest that the empirical likelihood ratio tests are more accurate (under the null hypothesis) and powerful (under the alternative hypothesis) than the normal approximation based tests of Chen et al. [Semiparametric of transformation models with censored data, Biometrika 89 (2002) 659-668] when the model is different from the proportional hazards model and the proportion of censoring is high.     

Incorporating causal structure and exogenous information with probabilistic models: With special reference to choice,gravity, migration,and Markov chains

Several methods of analyzing the parameters of the Semi‐Markov model and incorporating exogeneous information about its states are presented. Regression analysis of parameter estimates and doubly stochastic processes are discussed. Theoretical models for the structure of transition matrices, based on Luce's theory of choice behavior, are derived. Gravity models of varying specificity, useful in the study of migration, are special cases. Maximum likelihood procedures for estimating parameters, with fully operational numerical methods for solution of the likelihood equations, are given. Thus the integration of probabilistic models with theoretical and empirical analysis of causal structure, called for in the previous article, is carried out.     

我国宏观经济非参数联立模型的局部线性工具变量变窗宽估计

联立方程模型在经济政策制定、经济结构分析和预测方面起重要作用，目前关于非参数计量经济模型的研究主要停留在单方程模型上，而联立方程模型的研究在国际上刚刚起步，本将非参数回归模型的局部线性估计方法与传统联立方程模型估计方法相结合，首次提出了非参数计量经济联立模型的局部线性工具变量变窗宽估计并应用于我国宏观经济非参数联立模型，结果表明：我国宏观经济非参数联立模型优于线性联立模型且线性模型将造成不必要的人为设定误差；对于非参数联立模型，局部线性工具变量变窗宽估计优于局部线性估计。     

Interaction Model and Model Selection for Function-on-Function Regression

Regression models with interaction effects have been widely used in multivariate analysis to improve model flexibility and prediction accuracy. In functional data analysis, however, due to the challenges of estimating three-dimensional coefficient functions, interaction effects have not been considered for function-on-function linear regression. In this article, we propose function-on-function regression models with interaction and quadratic effects. For a model with specified main and interaction effects, we propose an efficient estimation method that enjoys a minimum prediction error property and has good predictive performance in practice. Moreover, converting the estimation of three-dimensional coefficient functions of the interaction effects to the estimation of two- and one-dimensional functions separately, our method is computationally efficient. We also propose adaptive penalties to account for varying magnitudes and roughness levels of coefficient functions. In practice, the forms of the models are usually unspecified. We propose a stepwise procedure for model selection based on a predictive criterion. This method is implemented in our R package FRegSigComp. Supplemental materials are available online.     

An empirical comparison of conventional techniques,neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data

The number of Non-Performing Loans has increased in recent years, paralleling the current financial crisis, thus increasing the importance of credit scoring models. This study proposes a three stage hybrid Adaptive Neuro Fuzzy Inference System credit scoring model, which is based on statistical techniques and Neuro Fuzzy. The proposed model’s performance was compared with conventional and commonly utilized models. The credit scoring models are tested using a 10-fold cross-validation process with the credit card data of an international bank operating in Turkey. Results demonstrate that the proposed model consistently performs better than the Linear Discriminant Analysis, Logistic Regression Analysis, and Artificial Neural Network (ANN) approaches, in terms of average correct classification rate and estimated misclassification cost. As with ANN, the proposed model has learning ability; unlike ANN, the model does not stay in a black box. In the proposed model, the interpretation of independent variables may provide valuable information for bankers and consumers, especially in the explanation of why credit applications are rejected.     

基于稳定分布的PARCH模型

本文首先介绍了稳定分布和基于正态分布、稳定分布的PARCH模型,并通过股票指数收益率的稳定化PP图和直方图发现其具有高峰厚尾特征.最后,通过上证指数的VaR计算,得到在金融风险度量中基于稳定分布的PARCH模型比基于正态分布的PARCH模型更加有效。