Congressional Election Forecasting: Structure-X Models for 2014

In the United States, election forecasting has expanded from First Generation to Second Generation approaches.1 The First Generation took hold in the early 1980s, and was dominated by a battle between structural modelers and pollsters (and to a lesser extent, the markets). The Second Generation, which had sunk deep roots by the 2012 presidential election, was dominated by Structuralists, Aggregators, Synthesizers, and Experts (as labeled by Lewis-Beck and Stegmaier 2014). The Structuralists continued in the tradition of issuing forecasts from static, single-equation explanatory models (e.g., Abramowitz 2014, Campbell 2014). The Aggregators departed from reliance on the polls of individual leading houses, instead freely combining many polls to come up with averaged, and dynamic, forecasts (e.g., Real Clear Politics). The Synthesizers joined structural models and poll aggregates to provide changing forecasts as Election Day approached (e.g., Erikson and Wlezien 2014; Linzer 2014). In contrast to this, The Experts, or Judges looked at whatever information they considered relevant, quantitative or not, arriving at forecasts shaped by current data and intuition (e.g., Cook and Wasserman 2014; Rothenberg 2014). The Second Generation work, at least some applications, produced successes without precedent when it came to presidential election forecasting. These successes have set the stage for breakthrough performances vis-à-vis congressional forecasting, an area long dominated by First Generation work (Abramowitz 2010; Campbell 2010). Of particular interest here is the Second Generation idea of combining methods (e.g., Erikson and Wlezien 2014, Graefe et al. 2014). In terms of the four methods described for the 2012 presidential election, the idea perhaps was most fully represented by joining structural modeling and poll aggregation. We propose to combine methods to generate House forecasts, uniquely bringing together structural modeling and expert judgment to create what we dub Structure-X models. The notion behind Structure-X models is simple. First, we forecast with a sound structural model and then “adjust” that forecast by considering expert predictions. These adjusted forecasts, it can be shown, dramatically reduce the error incurred from a structural model used alone. In the next section, we lay out our preferred structural model, based on the tried-and-true theory of congressional elections as referenda on the performance of the presidential party. We diagnose the strengths and weaknesses of the referenda model. Then, we test it, estimating stepahead forecasts of net presidential party seat change. Finally, we add forecast estimates derived from an expert, namely Stuart Rothenberg and his Rothenberg Political Report.2 We compare the accuracy of our forecasts with and without the expert, demonstrating that the addition of expert judgments easily cuts error in half. We conclude that combining sound theoretical structure with good, expert opinion can dramatically improve forecasting power. As an ex ante test, we apply this Structure-X method to the forecast of the 2014 congressional races. That forecast calls for a net seat loss of 15 Democratic House seats.