Bayesian forecasting of electoral outcomes with new parties’ competition
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DOI: 10.1016/j.ejpoleco.2019.01.006
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- Campbell, James E., 2008. "Evaluating U.S. presidential election forecasts and forecasting equations," International Journal of Forecasting, Elsevier, vol. 24(2), pages 259-271.
- Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
- José G. Montalvo, 2011. "Voting after the Bombings: A Natural Experiment on the Effect of Terrorist Attacks on Democratic Elections," The Review of Economics and Statistics, MIT Press, vol. 93(4), pages 1146-1154, November.
- Elinder, Mikael, 2010.
"Local economies and general elections: The influence of municipal and regional economic conditions on voting in Sweden 1985-2002,"
European Journal of Political Economy, Elsevier, vol. 26(2), pages 279-292, June.
- Elinder, Mikael, 2010. "Local Economies and General Elections: The Influence of Municipal and Regional Economic Conditions on Voting in Sweden 1985–2002," Working Paper Series 821, Research Institute of Industrial Economics.
- Ray C. Fair, 2009. "Presidential and Congressional Vote‐Share Equations," American Journal of Political Science, John Wiley & Sons, vol. 53(1), pages 55-72, January.
- Park, David K. & Gelman, Andrew & Bafumi, Joseph, 2004. "Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls," Political Analysis, Cambridge University Press, vol. 12(4), pages 375-385.
- Frederic Udina & Pedro Delicado, 2005.
"Estimating Parliamentary composition through electoral polls,"
Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 387-399, March.
- Frederic Udina & Pedro Delicado, 2001. "Estimating parliamentary composition through electoral polls," Economics Working Papers 562, Department of Economics and Business, Universitat Pompeu Fabra.
- Balaguer-Coll, Maria Teresa & Brun-Martos, María Isabel & Forte, Anabel & Tortosa-Ausina, Emili, 2015. "Local governments' re-election and its determinants: New evidence based on a Bayesian approach," European Journal of Political Economy, Elsevier, vol. 39(C), pages 94-108.
- Drew A. Linzer, 2013. "Dynamic Bayesian Forecasting of Presidential Elections in the States," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 124-134, March.
- Voia, Marcel-Cristian & Ferris, J. Stephen, 2013. "Do business cycle peaks predict election calls in Canada?," European Journal of Political Economy, Elsevier, vol. 29(C), pages 102-118.
- Daniel Stegmueller, 2013. "How Many Countries for Multilevel Modeling? A Comparison of Frequentist and Bayesian Approaches," American Journal of Political Science, John Wiley & Sons, vol. 57(3), pages 748-761, July.
- Gelman, Andrew & King, Gary, 1993. "Why Are American Presidential Election Campaign Polls So Variable When Votes Are So Predictable?," British Journal of Political Science, Cambridge University Press, vol. 23(4), pages 409-451, October.
- Douglas Hibbs, 2008. "Implications of the ‘bread and peace’ model for the 2008 US presidential election," Public Choice, Springer, vol. 137(1), pages 1-10, October.
- Lock, Kari & Gelman, Andrew, 2010. "Bayesian Combination of State Polls and Election Forecasts," Political Analysis, Cambridge University Press, vol. 18(3), pages 337-348, July.
- 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.
- Houshmand Shirani-Mehr & David Rothschild & Sharad Goel & Andrew Gelman, 2018. "Disentangling Bias and Variance in Election Polls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 607-614, April.
- Cook, R. Dennis & Ni, Liqiang, 2005. "Sufficient Dimension Reduction via Inverse Regression: A Minimum Discrepancy Approach," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 410-428, June.
- Wang, Wei & Rothschild, David & Goel, Sharad & Gelman, Andrew, 2015. "Forecasting elections with non-representative polls," International Journal of Forecasting, Elsevier, vol. 31(3), pages 980-991.
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Keywords
Multilevel models; Bayesian machine learning; Inverse regression; Evidence synthesis; Elections;All these keywords.
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