关于面板(纵向）数据的动态统计分析

项目名称： 关于面板(纵向）数据的动态统计分析

项目编号： No.11471068

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 数理科学和化学

项目作者： 高巍

作者单位： 东北师范大学

项目金额： 60万元

中文摘要： 由于面板（纵向）数据分析在经济学、金融学、基因疾病分析等方面有着广泛的应用，因此关于面板（纵向）数据的统计方法研究是大家非常感兴趣的问题之一。其中关于面板（纵向）数据的动态分析，尤为数量经济学家、统计学家所关注。目前对于线性动态模型的研究比较成熟，提出了工具变量方法和广义矩估计方法。但对于常用的非线性模型来说，仍然有许多问题亟待解决，其中最为关键的问题是消除模型中的个体效应，继而对感兴趣的动态参数进行估计和检验。 目前通常采用Baysian方法来消除模型中的个体效应，但对于短面板（纵向）数据来说，可能会由于选取不同个体效应先验分布会导致截然相反的结论，因此把个体效应看作是参数更容易被大家接受。此时由于参数的个数几乎与样本数一样多，问题变为 the incidental parameters问题。本项目将对非线性面板（纵向）动态模型进行研究，在研究中给出动态参数的估计方法和检验。

中文关键词： 动态模型；面板数据分析；离散数据分析；广义线性模型；假设检验

英文摘要： Panel(longitudinal) data analysis has wide applications in economics, finance, genetic studies for illness and so on, so it is one of interesting problems. For econometricians and statisticians, one of the concerned problems is to estimate its parameters in dynamic models for panel(longitudinal) data. There have made great contributions for linear dynamic models, and instrumental and generalized moment estimating methods have been proposed. But little progress have been made for many commonly used nonlinear dynamic models for panel(longitudinal) data. In oder to get consistent estimators for interesting parameters, the key to the problem is to delete individual effects from observed data. When individual effects are chosen as random variables, Bayesian methods is one of commomly used methods to delete individual effects for observed data, but contradictory conclusions may be led with the choice of different prior distributions for individual effects when the number of observations for individuals is small. So it is popular to treat individual effects as nuisance parameters. Then the number of parameters in models is almost the same as that of observed data and it is the incidental parameters problems, which now is one of the most concerned problems for statisticians and econometricians. In our research, we will propose methods to estimate the parameters in dynamic models for panel(longitudinal) data and test its related hypothesis.

英文关键词： Dynamic models;panel data;category data analysis;generalized linear models;hypothesis