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

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:
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
项目状态:
已结题
起止时间:
2009-09-15 至 2010-08-31

项目摘要

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中文摘要
翻译
博士论文研究:离散时间序列横截面模型离散时间序列横截面(TSCS)数据在包括政治学和经济学在内的各种学科中是一种非常重要的数据类型。TSCS数据既有时间维度,也有空间维度,因此具有丰富的分析结构。然而,这种相关的数据结构带来了多种可能的误差源,并提出了几个重要的方法论挑战。本研究将开发一种新的分析TSCS数据的方法: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水平模型,用于分析离散TSCS数据的时空相关性。该模型使实质性研究人员能够更好地理解调查中的动态过程,并进行比较研究。估计所提出的模型的参数和执行计算以便于模型比较是困难和计算量大的。本项目将通过设计一种特别关注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。为了方便模型的选择,本研究还将开发一种相对简单的计算贝叶斯因子的方法。一个设计良好、用户友好和开源的R包也将被提供给实施所建议的模型和方法的科学界。该项目将把该模型应用于国际政治经济学领域的几个重要问题。实质上,该模型将应用于政治学中的“民主优势”理论的研究。
英文摘要
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.
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Academic Centre of Excellence in Cyber Security Research - University of Oxford
  • 批准号:
    EP/R006784/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $10.44万
  • 财政年份:
    2017
  • 负责人:
    Andrew Martin
  • 依托单位:
Security and Privacy in Smart Grid Systems: Countermeasure and Formal Verification
  • 批准号:
    EP/N020170/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $25.86万
  • 财政年份:
    2016
  • 负责人:
    Andrew Martin
  • 依托单位:
Commercializing Abysis - an integrated resource for storing and analyzing antibody sequence and structure
  • 批准号:
    BB/K015443/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $20.55万
  • 财政年份:
    2013
  • 负责人:
    Andrew Martin
  • 依托单位:
Academic Centre of Excellence in Cyber Security Research -University of Oxford
  • 批准号:
    EP/K004778/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $6.49万
  • 财政年份:
    2012
  • 负责人:
    Andrew Martin
  • 依托单位:
海外基金