Forewarned Before Forecast: Presidential Election Forecasting Models and the 1992 Election.

1993 ◽  
Vol 26 (01) ◽  
pp. 17-21 ◽  
Author(s):  
Jay P. Greene
Keyword(s):  
Presidential Election ◽  
Election Forecasting ◽  
Forecasting Models

PROXY FORECASTS: A WORKING STRATEGY

2013 ◽  
Vol 46 (01) ◽  
pp. 39-40 ◽  
Author(s):  
Michael S. Lewis-Beck ◽  
Charles Tien
Keyword(s):  
Presidential Election ◽  
Point Estimate ◽  
Popular Vote ◽  
Election Forecasting ◽  
Proxy Model ◽  
Percentage Points ◽  
Estimate Error

Our Proxy Model of presidential election forecasting declared, from data issued six months before the November contest, that Obama would garner 52.7% of the two-party popular vote. [See the model release in ourMonkey Cageblog-post (Lewis-Beck and Tien 2012a) on September 18, 2012, and in the October issue ofPS(Lewis-Beck and Tien 2012b).] Thus, that forecast called the correct winner, with a point estimate error of only 0.9 percentage points.

Creating the Hybrid Field of Political Communication

2014 ◽  
Author(s):  
Kathleen Hall Jamieson
Keyword(s):  
Political Science ◽  
Political Communication ◽  
Presidential Election ◽  
Agenda Setting ◽  
Forecasting Models ◽  
Election Studies

This chapter tracks the byways that led to the emergence of a cross-disciplinary cadre of scholars identified with the hybrid field of political communication. Concentrating on the period between the Columbia election studies of the 1940s and 1993, it telegraphs the influence of the disciplines of sociology, political science, psychology and communication on the emerging field, indicates how scholars such as Elihu Katz, Kurt and Gladys Lang, Murray Edelman, and Doris Graber seeded the intellectual ground from which the field would grow, catalogues the emergence of a concept of effects that includes such phenomena as learning, the construction of political meaning, and agenda setting, and features a study that isolated the role of communication in activating the variables from which forecasting models predict presidential election outcomes.

Election Forecasting: The Future of the Presidency and the House

2008 ◽  
Vol 41 (04) ◽  
pp. 713-716 ◽  
Author(s):  
Brad Lockerbie
Keyword(s):  
Political Science ◽  
Annual Meeting ◽  
Presidential Election ◽  
Popular Vote ◽  
Election Forecasting ◽  
Vote Outcome ◽  
The Past ◽  
Actual Outcome ◽  
Variable Equation ◽  
Science Association

This article is about a simple two-variable equation forecasting presidential election outcomes and a three-variable equation forecasting seat change in House elections. Over the past two decades a cottage industry of political forecasting has developed (Lewis-Beck and Rice 1992; Campbell and Garand 2000). At the 1994 meeting of the Southern Political Science Association, several participants offered their forecasts of the upcoming midterm House elections. Unfortunately, not one of the forecasters was within 20 seats of the actual outcome. If, however, these forecasts had been pooled, as Gaddie (1997) points out, then they would have come remarkably close to the actual seat change that occurred. Moving forward, at the 1996 APSA Annual Meeting the collection of forecasters did a much better job with that year's presidential election. The forecasters also got the overall popular vote outcome correct at the 2000 APSA Annual Meeting for that year's presidential election. We all forecasted a victory for Al Gore, with James Campbell coming the closest to the actual total (50.2%) at 52.8%. At the panel at the 2004 APSA Annual Meeting almost every forecaster predicted the actual outcome correctly. Forecasting elections holds us accountable—we cannot go back and change our forecast for an election after it has occurred. Moreover, if we stick with one forecast, it easy to judge the overall accuracy of our equations.

Forecasting the 2012 French Presidential Election

2012 ◽  
Vol 45 (02) ◽  
pp. 218-222 ◽  
Author(s):  
Martial Foucault ◽  
Richard Nadeau
Keyword(s):  
Political Economy ◽  
Presidential Election ◽  
Right Wing ◽  
National Data ◽  
Election Forecasting ◽  
Vote Function ◽  
The Right ◽  
Nicolas Sarkozy

AbstractWho will win the next French presidential election? Forecasting electoral results from political-economy models is a recent tradition in France. In this article, we pursue this effort by estimating a vote function based on both local and national data for the elections held between 1981 and 2007. This approach allows us to circumvent the smallNproblem and to produce more robust and reliable results. Based on a model including economic (unemployment) and political (approval and previous results) variables, we predict the defeat, although by a relatively small margin, of the right-wing incumbent Nicolas Sarkozy in the second round of the French presidential election to be held in May 2012.

The Job of President and the Jobs Model Forecast: Obama for '08?

2008 ◽  
Vol 41 (04) ◽  
pp. 687-690 ◽  
Author(s):  
Michael S. Lewis-Beck ◽  
Charles Tien
Keyword(s):  
Social Sciences ◽  
Data Mining ◽  
Presidential Election ◽  
Model Forecast ◽  
Special Issue ◽  
Election Forecasting ◽  
The Social ◽  
Simple Product ◽  
Forecasting Errors

The statistical modelers are back. The presidential election forecasting errors of 2000 did not repeat themselves in 2004. On the contrary, the forecasts, from at least seven different teams, were generally quite accurate (Campbell 2004; Lewis-Beck 2005). Encouragingly, their prowess is receiving attention from forecasters outside the social sciences, in fields such as engineering and commerce. Noteworthy here is the recent special issue on U.S. presidential election forecasting published in theInternational Journal of Forecasting, containing some 10 different papers (Campbell and Lewis-Beck 2008). Our contribution in that special issue explored the question of whether our Jobs Model, off by only 1 percentage point in its 2004 forecast, was a simple product of data-mining (Lewis-Beck and Tien 2008).

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