Year: 2009
Type: report
Abstract: . Traditional election forecasting models are estimated from time-series data on relevant variables and that limits the type and number of variables that can be used. Index models do not suffer from the same restrictions. We used as many as 60 biographical variables to create an index model for forecasting U.S. Presidential Elections. For each candidate, we simply counted the number of variables for which the candidate was rated favorably. The index model forecast was that candidate A would win the popular vote if he had a higher index score than candidate B. We used simple linear regression to estimate a relationship between the index score of the candidate of the incumbent party and his share of the popular vote. We tested the model for the 29 U.S. presidential elections from 1896 to 2008. The model’s forecasts, calculated by cross-validation, correctly predicted the popular vote winner for 27 of the 29 elections and were more accurate than those from polls (15 out of 19), prediction markets (22 out of 26), and three regression models (12 to 13 out of 15 to 16). Out-of-sample forecasts of the two-party popular vote shares were more accurate for the last four elections from 1996 to 2008 than those from seven prominent regression models. By relying on different information and including more variables than traditional models, the biographical index model can improve the accuracy of long-term election forecasting. In addition, it can help parties to select the candidates running for office.
Authors: J. Scott Armstrong, Andreas Graefe
Language: English
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2162108049