Predicting Presidential Elections: An Evaluation of Forecasting

Abstract

Over the past two decades, a surge of interest in the area of forecasting has produced a number of statistical models available for predicting the winners of U.S. presidential elections. While historically the domain of individuals outside the scholarly community - such as political strategists, pollsters, and journalists - presidential election forecasting has become increasingly mainstream, as a number of prominent political scientists entered the forecasting arena. With the goal of making accurate predictions well in advance of the November election, these forecasters examine several important election "fundamentals" previously shown to impact national election outcomes. In general, most models employ some measure of presidential popularity as well as a variety of indicators assessing the economic conditions prior to the election. Advancing beyond the traditional, non-scientific approaches employed by prognosticators, politicos, and pundits, today's scientific models rely on decades of voting behavior research and sophisticated statistical techniques in making accurate point estimates of the incumbent's or his party's percentage of the popular two-party vote. As the latest evolution in presidential forecasting, these models represent the most accurate and reliable method of predicting elections to date. This thesis provides an assessment of forecasting models' underlying epistemological assumptions, theoretical foundations, and methodological approaches. Additionally, this study addresses forecasting's implications for related bodies of literature, particularly its impact on studies of campaign effects.