The 2020 Election In The United States: Beta Regression Versus Regression Quantiles

0548703 - ÚI 2022 CZ eng A - Abstract
Kalina, Jan
The 2020 Election In The United States: Beta Regression Versus Regression Quantiles.
14th International Scientific Conference RELIK 2021. Book of Abstracts. Prague: Prague University of Economics and Business, 2021 - (Vrabcová, J.; Langhamrová, J.). s. 34-34
[RELIK 2021: Reproduction of Human Capital - mutual links and connections. 04.11.2021-05.11.2021, Praha]
Institutional support: RVO:67985807
Keywords : elections results * electoral demography * quantile regression * heteroscedasticity, outliers

The results of the presidential election in the United States in 2020 desire a detailed statistical analysis by advanced statistical tools, as they were much different from the majority of available prognoses as well as from the presented opinion polls. We perform regression modeling for explaining the election results by means of three demographic predictors for individual 50 states: weekly attendance at religious services, percentage of Afroamerican population, and population density. We compare the performance of beta regression with linear regression, while beta regression performs only slightly better in terms of predicting the response. Because the United States population is very heterogeneous and the regression models are heteroscedastic, we focus on regression quantiles in the linear regression model. Particularly, we develop an original quintile regression map; such graphical visualization allows to perform an interesting interpretation of the effect of the demographic predictors on the election outcome on the level of individual states.
Permanent Link: http://hdl.handle.net/11104/0324756