Donald Trump's success in the 2016 presidential primary election prompted scrutiny for the role of news media in elections. Was Trump successful because news media publicized his campaign and crowded out coverage of other candidates? We examine the dynamic relationships between media coverage, public interest, and support for candidates in the time preceding the 2016 Republican presidential primary to determine (1) whether media coverage drives support for candidates at the polls and (2) whether this relationship was different for Trump than for other candidates. We find for all candidates that the quantity of media coverage had significant and long-lasting effects on public interest in that candidate. Most candidates do not perform better in the polls following increases in media coverage. Trump is an exception to this finding, receiving a modest polling bump following an increase in media coverage. These findings suggest that viability cues from news media contributed to Trump's success and can be influential in setting the stage in primary elections.
Researchers face a tradeoff when applying latent variable models to time-series, cross-sectional data. Static models minimize bias but assume data are temporally independent, resulting in a loss of efficiency. Dynamic models explicitly model temporal data structures, but smooth estimates of the latent trait across time, resulting in bias when the latent trait changes rapidly. We address this tradeoff by investigating a new approach for modeling and evaluating latent variable estimates: a robust dynamic model. The robust model is capable of minimizing bias and accommodating volatile changes in the latent trait. Simulations demonstrate that the robust model outperforms other models when the underlying latent trait is subject to rapid change, and is equivalent to the dynamic model in the absence of volatility. We reproduce latent estimates from studies of judicial ideology and democracy. For judicial ideology, the robust model uncovers shocks in judicial voting patterns that were not previously identified in the dynamic model. For democracy, the robust model provides more precise estimates of sudden institutional changes such as the imposition of martial law in the Philippines (1972–1981) and the short-lived Saur Revolution in Afghanistan (1978). Overall, the robust model is a useful alternative to the standard dynamic model for modeling latent traits that change rapidly over time.