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Aggregating qualitative district-level campaign assessments to forecast election results: Evidence from Japan

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  • UMEDA, Michio

Abstract

The poll aggregation is conducted in the USA and European democracies for electoral forecasting. However, this has not been the case in Japan because the news media report on electoral campaigns with qualitative assessments rather than poll numbers, although these assessments are based on extensive polling. Our study developed an approach to aggregate qualitative district-level election campaign coverage in Japan, applied the method to forecast the outcomes of the 2017 general election for Japan’s Lower House of the National Diet, and assessed the accuracy of the forecast against the actual results. We integrated the qualitative assessments by using the item response theory, which effectively predicted the electoral results. The method proposed in this paper can be applied to aggregate qualitative assessments by experts in other countries, such as the Cook Political Report in the USA. This would improve the accuracy of election forecasting when combined with existing approaches.

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  • UMEDA, Michio, 2023. "Aggregating qualitative district-level campaign assessments to forecast election results: Evidence from Japan," International Journal of Forecasting, Elsevier, vol. 39(2), pages 956-966.
    • Handle: RePEc:eee:intfor:v:39:y:2023:i:2:p:956-966
    DOI: 10.1016/j.ijforecast.2022.03.006
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    1. Martin, Andrew D. & Quinn, Kevin M., 2002. "Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999," Political Analysis, Cambridge University Press, vol. 10(2), pages 134-153, April.
    2. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzán, Alfred G., 2014. "Combining forecasts: An application to elections," International Journal of Forecasting, Elsevier, vol. 30(1), pages 43-54.
    3. Lewis-Beck, Michael S. & Tien, Charles, 2012. "Japanese election forecasting: Classic tests of a hard case," International Journal of Forecasting, Elsevier, vol. 28(4), pages 797-803.
    4. Graefe, Andreas, 2019. "Accuracy of German federal election forecasts, 2013 & 2017," International Journal of Forecasting, Elsevier, vol. 35(3), pages 868-877.
    5. SHOR, BORIS & McCARTY, NOLAN, 2011. "The Ideological Mapping of American Legislatures," American Political Science Review, Cambridge University Press, vol. 105(3), pages 530-551, August.
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