This paper develops a recursive model of voter turnout and voting outcomes at U.S. county level to investigate the socioeconomic determinants of recent U.S. presidential elections. It is shown that the relationship between many socioeconomic variables and voting outcomes is not uniform across U.S. regions. By allowing for regional heterogeneity and using high-dimensional variable selection algorithms, we can explain and correctly predict the unexpected 2016 Republican victory. Key factors explaining voting outcomes include incumbency effects, voter turnout, local economic performance, unemployment, poverty, educational attainment, house price changes, urban-rural scores, and international competitiveness. Our results corroborate evidence of 'short-memory' among voters: economic fluctuations realized a few months prior to the election are indeed powerful predictors of voting outcomes as compared to their longer- term analogues. The paper then presents real time forecasts for the 2020 U.S. Presidential Election based on data available at the end of July 2020 which are then updated based on data available as of mid-October.
voter turnout popular and electoral college votes simultaneity and recursive identification high dimensional forecasting models Lasso OCMT