Improving election prediction internationally

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Ryan Kennedy, Stefan Wojcik, David Lazer
Journal/Conference Name Science
Paper Category
Paper Abstract This study reports the results of a multiyear program to predict direct executive elections in a variety of countries from globally pooled data. We developed prediction models by means of an election data set covering 86 countries and more than 500 elections, and a separate data set with extensive polling data from 146 election rounds. We also participated in two live forecasting experiments. Our models correctly predicted 80 to 90% of elections in out-of-sample tests. The results suggest that global elections can be successfully modeled and that they are likely to become more predictable as more information becomes available in future elections. The results provide strong evidence for the impact of political institutions and incumbent advantage. They also provide evidence to support contentions about the importance of international linkage and aid. Direct evidence for economic indicators as predictors of election outcomes is relatively weak. The results suggest that, with some adjustments, global polling is a robust predictor of election outcomes, even in developing states. Implications of these findings after the latest U.S. presidential election are discussed.
Date of publication 2017
Code Programming Language R

Copyright Researcher 2022