标题： 从历史和当代来源估算GDP、人均GDP和人口 显示英文标题

标题： Latent Estimation of GDP, GDP per capita, and Population from Historic and Contemporary Sources

摘要： 国内生产总值（GDP）、人均GDP和人口的概念是政治学和经济学研究的核心。 然而，越来越多的文献表明，现有这些概念的测量方法存在相当大的误差，或者基于过于简化的建模选择。 我们通过创建一个动态的三维潜在特质模型来解决这些问题，该模型利用有关GDP、人均GDP和人口的观察信息，为这些重要概念估计后验预测区间。 通过结合历史和当代的信息来源，我们能够将现有国家-年份数据集的时间和空间覆盖范围从公元1500年扩展到2015年。 由于该模型使用了潜在概念的多个指标，我们可以估计不同国家-年份估算值的相对精确度。 总体而言，我们的潜在变量模型提供了一种将来自不同历史和当代数据源的信息纳入其中的原则性方法。 随着研究人员发现关于这些概念的新来源或替代来源，它可以被扩展或细化。 显示英文摘要

摘要： The concepts of Gross Domestic Product (GDP), GDP per capita, and population are central to the study of political science and economics. However, a growing literature suggests that existing measures of these concepts contain considerable error or are based on overly simplistic modeling choices. We address these problems by creating a dynamic, three-dimensional latent trait model, which uses observed information about GDP, GDP per capita, and population to estimate posterior prediction intervals for each of these important concepts. By combining historical and contemporary sources of information, we are able to extend the temporal and spatial coverage of existing datasets for country-year units back to 1500 A.D through 2015 A.D. and, because the model makes use of multiple indicators of the underlying concepts, we are able to estimate the relative precision of the different country-year estimates. Overall, our latent variable model offers a principled method for incorporating information from different historic and contemporary data sources. It can be expanded or refined as researchers discover new or alternative sources of information about these concepts.