Using Online Search Traffic to Predict US Presidential Elections

Abstract

Predictions of the United States presidential election vote outcome have been growing in scope and popularity in the academic realm. Traditional election forecasting models predict the United States presidential popular vote outcome on a national level based primarily on economic indicators (e.g., real income growth, unemployment), public approval ratings, and incumbency advantage. Many of these forecasting models are rooted in retrospective voting theory (Downs 1957; Fiorina 1981), essentially rewarding the party in office if times are good, punishing it if times are bad. These models have successfully predicted election results by modeling economic performance and incumbent approval ratings (Campbell 2012; Fair 1992; Fair 1996; Klarner 2012). For example, Abramowitz's (2004; 2005) “time for a change model” predicts election results using economic performance during the first half of the election year, the number of years the incumbent party has been in office, and presidential approval. For a full review of 13 presidential forecasts for the US 2012 election, seePS: Political Science and PoliticsOctober 2012 (45 (4): 610–75). Although national models are the most common, researchers have also started to use state-level predictions for presidential and congressional outcomes, with mostly positive success (Berry and Bickers 2012; Jerome and Jerome-Speziari 2012; Klarner 2012; Silver 2012). These models use similar predictors, such as incumbency, economic conditions, and home-state advantage, and predict the per-candidate percentage of popular vote. Unfortunately, with state-level models, many of the economic variables used in predicting national models are unavailable beyond 10–15 election cycles (compounded also by 1959 additions of Alaska and Hawaii), so state-level models naturally have a shorter period of analysis than do national models.