First, make sure to check out this great piece explaining what exactly differentiates Nate Silver from conventional notions of forecasting, that is, what differentiates theoretical forecasting from fitted forecasting. Afterward, here are five political scientists or groups of political scientists who specialize in forecasting and what they see as likely outcomes of today’s voting. Their methods vary widely, but most of these predictions were made before Labor Day, so it will be interesting to see how they work out – and how they stack up against each other. Most of these models were first presented at the 2010 APSA annual meeting. You’ll notice that a couple of these models’ House outcomes don’t sum to 435 – this is either because they use Monte Carlo simulating, in which case I took average outcomes, or have some other methodology explained in the paper linked from their predictions.
PEC missed the Presidential EV in 2008 by one vote, hit the House spot on, and missed one Senate seat. They have what looks like a fairly optimistic showing for Republicans today, but as a pollster I’m inclined to like their poll-based methodology.
The House forecast is fairly close to the forecasts made by a number of prominent political analysts that are based on more informal judgments about the national political environment and assessments of individual House races (Cook 2010; Wood 2010). However, the forecast for Democratic losses in the Senate is somewhat lower than that formulated by most of these forecasters. Democrats appear to have substantially more Senate seats at risk than Republicans in this election cycle, even though both parties have 18 seats up for election (Sabato 2010).
The foregoing analysis suggests that when it comes to the race for control of the House of Representatives, the Democrats have the edge. They stand to lose 11% to 12% of their representation, which is average for a midterm election (M = 12%, SD = 9). That number would not be enough to dislodge Nancy Pelosi from the Speaker’s chair. However, there is a nontrivial chance that 40 or more of their members will be defeated in November, an outcome that would reduce them to minority status.
[W]e should be wary of the possibility that the underlying model of the national vote works differently in 2010 or is influenced by variables we have not taken into account. In particular, we note that in past elections, the out-party usually has increased its support in the generic polls as the election year campaign has progressed. But this increase did not happen in the first six months of 2010. The generic polls have hovered around a 50-50 division rather than sliding further in the Republicans’ favor, which may reflect a strong public engagement in the campaign far earlier than has occurred in the run-up to previous midterms.
We argue that forecasting models derived from theory are to be favored over those models that are derived from fit, including those that we have dubbed tracking models. Models that aim at explanation rather than prediction should accomplish the task of prediction better. The referendum model we offer derives from strong theory about the workings of the political economy during elections. Congressional voters perceive how the president and his party are handling the major economic and political issues of the day. If they approve, they stay with his party at the ballot box; if they do not, they vote against his party. At the time of midterms, the electorate’s assessment is especially critical, meaning the president’s party almost never escapes heavy additional punishment. In 2010, these forces have all come together in negative ways, foretelling a Democratic loss of 22 seats.
Of these five forecasts, the average outcome is 221 Democrats in the House and 212 Republicans in the house. While only two of them made explicit Senate predictions, virtually everyone involved in predicting election outcomes believes that the Republicans will not win the majority in the Senate. Remember to go vote today. Tomorrow we’ll be taking a peek into the black box that is John’s brain and seeing how my own methodology stacked up overall.