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Title: Replication data for: The Limits of Prediction: Incorporating Uncertainty in a Normal Vote Model      
dateReleased:
04-28-2010
downloadURL: http://hdl.handle.net/1902.1/14587
ID:
hdl:1902.1/14587
description:
Population forecasts suggest that the redistribution of the electoral college following Census 2010 and 2020 will likely benefit Southern and Western states at the expense of Northeastern states. In the short term, an electoral gain for strongly Republican states like Texas and Arizona may benefit Republican presidential candidates. But what can these population forecasts tell us about the major parties' long term electoral prospects? Burmila (2009) develops an updated model of Converse's (1966) "normal vote" to make inferences about the effect of electoral vote changes on the 2012-2028 presidential elections. Here, we test Burmila's model against known election results to derive a measure of the model's uncertainty. Our results suggest that a normal vote model (1) does not take into account the variance in state voting patterns, (2) does not make clear the limitations of its predictive power as it is applied farther in the future, and (3) is overly dependent on recent election results. We develop an improved model that corrects for these limitations. We find that the short-term boost to the Republican party is likely smaller than Burmila anticipates, and that long term predictions are too uncertain to report. We conclude by suggesting a more dynamic model of voting trends that accounts for changing demographics in temporally-distant predictions.
description:
Krista Loose; Vanessa Williamson, 2010, "Replication data for: The Limits of Prediction: Incorporating Uncertainty in a Normal Vote Model", http://hdl.handle.net/1902.1/14587, Harvard Dataverse, V1
name:
Krista Loose
Vanessa Williamson
homePage: http://www.harvard.edu/
name:
Harvard University
ID:
SCR:011273
abbreviation:
DataVerse
homePage: http://thedata.org/
name:
Dataverse Network Project
ID:
SCR:001997