Date of Graduation
8-2019
Document Type
Thesis
Degree Name
Master of Science in Statistics and Analytics (MS)
Degree Level
Graduate
Department
Statistics and Analytics
Advisor/Mentor
Petris, Giovanni G.
Committee Member
Chakraborty, Avishek A.
Second Committee Member
Tipton, John R.
Abstract
Our goal is to create spatio-temporal models for predicting future gubernatorial elections. For a concrete example of how well our models work we use past data to predict the 2018 Arkansas gubernatorial election and use the existing 2018 election data to check our models predictive accuracy. Gubernatorial election data was collected from the Arkansas Secretary of State website while related covariate data was collected from the website for the Federal Reserve Bank of St. Louis. The data we collect is on the county level. For predictive purposes we fit multiple models to the data using Markov chain Monte Carlo and compare each model to determine which has the best predictive ability.
Citation
Harris, M. (2019). Spatio-Temporal Prediction of Arkansas Gubernatorial Election. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/3414
