Doctoral Dissertation Research: Discrete Time-Series Cross-Section Models of Political Economy

博士论文研究：政治经济学的离散时间序列横截面模型

• 批准号: 0918320
• 负责人: Andrew Martin
• 金额: $ 0.7万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0918320&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Discrete; Time

Doctoral Dissertation Research: Discrete Time-Series Cross-Section Models of Political EconomyDiscrete time-series cross-sectional (TSCS) data are a very important type of data in a variety of disciplines, including political science and economics. TSCS data have both a time and spatial dimension and are thus rich in structure for analysis. However, this correlated data structure invites multiple possible sources of error and raises several important methodological challenges.This research will develop a new method for analyzing TSCS data: a Bayesian generalized linear multilevel model with two-dimensional random effects and p-th order autoregressive errors for analyzing the temporal and spatial dependence of discrete TSCS data. The model empowers substantive researchers to better understand the dynamic process under investigation and to conduct comparative studies. Estimating the parameters of the proposed model and performing computations to facilitate model comparison are difficult and computationally intense. This project will solve these technical difficulties by designing a hybrid Markov Chain Monte Carlo algorithm with a special focus on simulation efficiency. To facilitate model choice, this research will also develop a relatively easy means of computing Bayes factors. A well-designed, user-friendly, and open source R package will also be provided to the scientific community that implements the proposed model and methods.This project will apply the model to several important questions in the field of international political economy. Substantively, the model will be applied to studying the "democratic advantage" theory in political science.

博士论文研究：政治经济学的离散时间序列横截面模型离散时间序列横截面（TSCS）数据是包括政治学和经济学在内的各种学科中非常重要的数据类型。 TSCS 数据具有时间和空间维度，因此具有丰富的分析结构。 然而，这种相关的数据结构会带来多种可能的误差源，并提出一些重要的方法学挑战。本研究将开发一种分析 TSCS 数据的新方法：具有二维随机效应和 p 阶自回归误差的贝叶斯广义线性多级模型，用于分析离散 TSCS 数据的时间和空间依赖性。 该模型使实质性研究人员能够更好地理解所调查的动态过程并进行比较研究。 估计所提出模型的参数并执行计算以促进模型比较是困难且计算量大的。 该项目将通过设计一种特别注重仿真效率的混合马尔可夫链蒙特卡罗算法来解决这些技术难题。 为了方便模型选择，本研究还将开发一种相对简单的计算贝叶斯因子的方法。 还将向科学界提供一个设计良好、用户友好且开源的 R 包来实现所提出的模型和方法。该项目将将该模型应用于国际政治经济领域的几个重要问题。实质上，该模型将应用于研究政治学中的“民主优势”理论。