Improving predictive accuracy of exit polls

Jose M. Pavia

International Journal of Forecasting, 2010, vol. 26, issue 1, 68-81

Abstract: Exit polls are best known for their use in election forecasting. In recent years, however, some prominent mistaken predictions have been made, undermining public confidence in the accuracy of both exit polls and survey methods. Nonresponse bias has been claimed as being one of the main reasons for inaccurate projections. Traditionally, the issue has been handled through an age-race-sex adjustment at the national and state levels. An alternative solution is suggested and detailed in this paper. A two-step strategy is proposed to reduce nonresponse bias and improve predictions. First, "vote-remembering" (vote recall) is used to correct party proportion estimates at polling locations; second, this is used to estimate party proportions at precinct level through a regression estimator. The method is gauged by forecasting the 2003 and 2007 Corts Valencianes elections using raw data from the exit polls conducted by SigmaDos for Generalitat Valenciana. In light of the results, this procedure considerably improves raw data projections and shows a substantial improvement over industry (SigmaDos) forecasts. It therefore represents an interesting alternative that could easily be adopted for exit polling in any country where precinct-level voting data exist.

Keywords: Election; forecasts; Exit; polling; Evaluating; forecasts; Survey; sampling; Vote-remembering; Vote; recall; Spanish; elections (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169-2070(09)00072-7
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:26:y::i:1:p:68-81

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().