Graefe, Andreas and Armstrong, J. Scott and Jones, Randall J. and Cuzan, Alfred G. (2017): Assessing the 2016 U.S. Presidential Election Popular Vote Forecasts. Published in: The 2016 Presidential Election: The causes and consequences of an Electoral Earthquake (4 October 2017)
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Abstract
The PollyVote uses evidence-based techniques for forecasting the popular vote in presidential elections. The forecasts are derived by averaging existing forecasts generated by six different forecasting methods. In 2016, the PollyVote correctly predicted that Hillary Clinton would win the popular vote. The 1.9 percentage-point error across the last 100 days before the election was lower than the average error for the six component forecasts from which it was calculated (2.3 percentage points). The gains in forecast accuracy from combining are best demonstrated by comparing the error of PollyVote forecasts with the average error of the component methods across the seven elections from 1992 to 2012. The average errors for last 100 days prior to the election were: public opinion polls (2.6 percentage points), econometric models (2.4), betting markets (1.8), and citizens’ expectations (1.2); for expert opinions (1.6) and index models (1.8), data were only available since 2004 and 2008, respectively. The average error for PollyVote forecasts was 1.1, lower than the error for even the most accurate component method.
Item Type: | MPRA Paper |
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Original Title: | Assessing the 2016 U.S. Presidential Election Popular Vote Forecasts |
Language: | English |
Keywords: | election, forecasting, voting |
Subjects: | C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods D - Microeconomics > D7 - Analysis of Collective Decision-Making > D72 - Political Processes: Rent-Seeking, Lobbying, Elections, Legislatures, and Voting Behavior |
Item ID: | 83282 |
Depositing User: | J Armstrong |
Date Deposited: | 14 Dec 2017 05:43 |
Last Modified: | 26 Sep 2019 21:33 |
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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/83282 |