Title: Latent Estimation of GDP, GDP per capita, and Population from Historic and Contemporary Sources,
Abstract: 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.