基于Gibbs抽样算法的贝叶斯动态面板数据模型分析
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引用本文:朱慧明,周帅伟,李素芳,曾昭法.基于Gibbs抽样算法的贝叶斯动态面板数据模型分析[J].经济数学,2011,(1):52-60
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作者单位
朱慧明,周帅伟,李素芳,曾昭法 (1.湖南大学工商管理学院湖南 长沙4100822.湖南大学金融与统计学院湖南 长沙410079) 
中文摘要:针对现有动态面板数据分析中存在偶发参数和没有考虑模型参数的不确定性风险问题,提出了基于Gibbs抽样算法的贝叶斯随机系数动态面板数据模型.假设初始值服从平稳分布,自回归系数服从Logit正态分布的条件下,设计了Markov链Monte Carlo数值计算程序,得到了模型参数的贝叶斯估计值.实证研究结果表明:基于Gibbs抽样方法的贝叶斯动态面板回归模型能有效地揭示跨截面滞后变量对响应变量的位置、尺度和形状的影响.
中文关键词:动态面板数据  MCMC  Gibbs抽样算法  贝叶斯推断  后验分布
 
Bayesian Analysis for Dynamic Panel Data Models Using Gibbs Sampling Algorithm
Abstract:We developed the hierarchical Bayesian approach for inference in random coefficient dynamic panel data models. Our approach allows for the initial values of each unit's process to be correlated with the unit specific coefficients.A stationarity assumption was imposed on each unit's process by assuming that the unit specific autoregressive coefficient is drawn from a logitnormal distribution. The research shows that our approach can efficiently reveal how the lagged variables affect the location, scale and shape of the response variable across section.
keywords:dynamic panel data  MCMC  Gibbs sampling algorithm  Bayesian inference  Posterior distribution
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