Forecasting National Medal Totals at the Summer Olympic Games Reconsidered
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DOI: 10.1111/ssqu.12782
Note: View the original document on HAL open archive server: https://hal.science/hal-03206951
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- Nicolas Scelles & Wladimir Andreff & Liliane Bonnal & Madeleine Andreff & Pascal Favard, 2020. "Forecasting National Medal Totals at the Summer Olympic Games Reconsidered," Social Science Quarterly, Southwestern Social Science Association, vol. 101(2), pages 697-711, March.
- Nicolas Scelles & Wladimir Andreff & Liliane Bonnal & Madeleine Andreff & Pascal Favard, 2020. "Forecasting National Medal Totals at the Summer Olympic Games Reconsidered," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03206951, HAL.
References listed on IDEAS
- Vagenas, George & Vlachokyriakou, Eleni, 2012. "Olympic medals and demo-economic factors: Novel predictors, the ex-host effect, the exact role of team size, and the “population-GDP” model revisited," Sport Management Review, Elsevier, vol. 15(2), pages 211-217.
- Marcus Noland & Kevin Stahler, 2016.
"What Goes into a Medal: Women's Inclusion and Success at the Olympic Games,"
Social Science Quarterly, Southwestern Social Science Association, vol. 97(2), pages 177-196, June.
- Marcus Noland & Kevin Stahler, 2014. "What Goes into a Medal: Women's Inclusion and Success at the Olympic Games," Working Paper Series WP14-7, Peterson Institute for International Economics.
- Leeds Eva Marikova & Leeds Michael A., 2012. "Gold, Silver, and Bronze: Determining National Success in Men’s and Women’s Summer Olympic Events," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(3), pages 279-292, June.
- Pravin K. Trivedi & David M. Zimmer, 2014. "Success at the Summer Olympics: How Much Do Economic Factors Explain?," Econometrics, MDPI, vol. 2(4), pages 1-34, December.
- Aaron Lowen & Robert O. Deaner & Erika Schmitt, 2016. "Guys and Gals Going for Gold," Journal of Sports Economics, , vol. 17(3), pages 260-285, April.
- Andrew B. Bernard & Meghan R. Busse, 2004. "Who Wins the Olympic Games: Economic Resources and Medal Totals," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 413-417, February.
- Forrest, David & Sanz, Ismael & Tena, J.D., 2010. "Forecasting national team medal totals at the Summer Olympic Games," International Journal of Forecasting, Elsevier, vol. 26(3), pages 576-588, July.
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Cited by:
- David Forrest & J. D. Tena & Carlos Varela-Quintana, 2023. "The influence of schooling on performance in chess and at the Olympics," Empirical Economics, Springer, vol. 64(2), pages 959-982, February.
- Schlembach, Christoph & Schmidt, Sascha L. & Schreyer, Dominik & Wunderlich, Linus, 2022. "Forecasting the Olympic medal distribution – A socioeconomic machine learning model," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
- Christoph Schlembach & Sascha L. Schmidt & Dominik Schreyer & Linus Wunderlich, 2020. "Forecasting the Olympic medal distribution during a pandemic: a socio-economic machine learning model," Papers 2012.04378, arXiv.org, revised Jun 2021.
- Emilio Gómez-Déniz & Nancy Dávila-Cárdenes & Alejandro Leiva-Arcas & María J. Martínez-Patiño, 2021. "Measuring Efficiency in the Summer Olympic Games Disciplines: The Case of the Spanish Athletes," Mathematics, MDPI, vol. 9(21), pages 1-15, October.
- Carl Singleton & J. James Reade & Johan Rewilak & Dominik Schreyer, 2021. "How big is home advantage at the Olympic Games?," Economics Discussion Papers em-dp2021-13, Department of Economics, University of Reading.
- David Forrest & J.D Tena & Carlos Varela-Quintana, 2020. "Who wins at the Chess Olympics? The role of resources and education capital," Working Papers 202013, University of Liverpool, Department of Economics.
- Franklin G. Mixon Jr. & Richard J. Cebula, 2022. "Property Rights Freedom and Innovation: Eponymous Skills in Women's Gymnastics," Journal of Sports Economics, , vol. 23(4), pages 407-430, May.
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This paper has been announced in the following NEP Reports:- NEP-FOR-2021-05-10 (Forecasting)
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