Prediction for Canadian federal election aided by Canadian Community Health Survey

Author: 
Date created: 
2019-09-05
Identifier: 
etd20546
Keywords: 
General linear mixed-effects model
Joint modeling
Logistic regression model
Monte Carlo EM algorithm
Two-stage estimation
Abstract: 

This project aims to develop predictive models for Canadian federal elections. We begin with explanatory analyses of two sets of data: some publicly accessible election data and some extracted data from the Canadian Community Health Survey (CCHS) 2007-2018 on life satisfaction and other potentially associated social-demographics. We propose to predict for federal election outcomes using the information on longitudinal Canadian life satisfaction. Specifically, we model the federal election outcome for a riding in change from its previous election jointly with its longitudinal life satisfaction since the previous election. Election data from years 2008 and 2011 and the CCHS data of 2008-2011 are employed to fit the model via both the two-stage estimation and the maximum likelihood estimation by the Monte Carlo EM algorithm. The analysis results indicate that life satisfaction is an important factor in election prediction. It appears that young adults are more likely to vote for a change but male voters are less likely to do so. Using voter information or CCHS respondent's information to model the election outcomes produce different estimation results. Two applications of the proposed approach are presented to further illustrate the proposed joint modeling approach.

Document type: 
Graduating extended essay / Research project
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Senior supervisor: 
X. Joan Hu
Department: 
Science: Department of Statistics and Actuarial Science
Thesis type: 
(Project) M.Sc.
Statistics: