[gov3009-l] Lok on "Bayesian Combination of State Polls and Election Forecasts"

Dear Applied Statistics Community, The first meeting of the applied statistics workshop will be this Wednesday, February 4th, when Kari Lock, Graduate Student in the Department of Statistics, will present "Bayesian Combination of State Polls and Election Forecasts". Kari provided the following abstract: A wide range of potentially useful data are available for election forecasting: the results of previous elections, a multitude of pre-election polls, and predictors such as measures of national and statewide economic performance. How accurate are different forecasts? We estimate predictive uncertainty via analysis of data collected from past elections (actual outcomes, pre-election polls, and model estimates). With these estimated uncertainties, we use Bayesian inference to integrate the various sources of data to form posterior distributions for the state and national two-party Democratic vote shares for the 2008 election. Our key idea is to separately forecast the national popular vote shares and the relative positions of the states. The Applied Statistics Workshop meets each Wednesday at 12 noon in K-354 CGIS-Knafel (1737 Cambridge St). The workshop begins with a light lunch and presentations usually start around 1215 and last until about 130 pm. Cheers, Justin Grimmer