Bayesian Methods for Economic Panel Data

在本研究中，我考虑了使用马尔可夫链蒙特卡罗（MCMC）的经济面板数据的贝叶斯估计方法。首先，我提出了一个贝叶斯方法来估计由Deaton和Muellbauer提出的几乎理想的需求系统，使用面板数据，包括零支出。我明确地处理了模型中的零支出问题，并估计了满足累加条件的几乎理想的需求系统。然后，利用MCMC估计了商品消费、总消费和等价规模等不可观测参数，并利用它们的后验分布计算了不平等测度、总消费弹性和价格弹性。接着，提出了基于Hausman和Taylor模型的静态伪面板模型的贝叶斯方法。在此基础上，将静态伪面板模型扩展到动态伪面板模型，提出了贝叶斯估计方法。值得注意的是，在贝叶斯方法中，没有额外的变化数据（即，工具变量）与非贝叶斯方法相比是必需的。我写了三篇关于这个项目的文章。其中一个已经提交给一个国际期刊。我让其他人准备投降。

In this research, I considered the Bayesian estimation methods for economic panel data using Markov chain Monte Carlo (MCMC). First, I proposed a Bayesian method for estimating the almost ideal demand system proposed by Deaton and Muellbauer using the panel data that includes zero expenditures. I dealt explicitly with the problem of zero expenditures in the model and estimate the almost ideal demand system that satisfy the adding-up condition. Furthermore, using MCMC, I estimated unobservable parameters including consumption of commodities, total consumption and equivalence scale and used their posterior distributions to calculate inequality measures, total consumption elasticities, and price elasticities.Next, I proposed the Bayesian method for the static pseudo panel model which is based on Hausman and Taylor model. Further, I extended the static pseudo panel model to the dynamic pseudo panel model and proposed the Bayesian estimation method. It is noteworthy that in the Bayesian approach no additional variation data (i.e., instrumental variables) are required in contrast with non-Bayesian approach.I wrote three articles on this project. One of them has already submitted to an international journal. I make the others ready to submit.