Using a nested logit model to forecast television ratings
AbstractThe television environment has become increasingly complex over the past decade, but scant attention has been paid to the modeling and forecasting of television ratings. In this study we use a little-known version of the nested logit model that is suitable for aggregate choice decision data, since television ratings are aggregate measures. We extend this model to include television program random effects, and develop a novel method for predicting program random effects for programs that have not previously been broadcast. Our dataset is comprehensive, spanning the period 2004–2008, and has program ratings for each main broadcaster, as well as some satellite channels, in a market with over 70 channels. We compare our model’s forecasts with those of several other models and show that it markedly outperforms these models.
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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 28 (2012)
Issue (Month): 3 ()
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Web page: http://www.elsevier.com/locate/ijforecast
TV ratings forecasting; Nested logit model; Random effects;
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- Dubin, Jeffrey A, et al, 1992. "The Demand for Tax Return Preparation Services," The Review of Economics and Statistics, MIT Press, vol. 74(1), pages 75-82, February.
- Roland T. Rust & Mark I. Alpert, 1984. "An Audience Flow Model of Television Viewing Choice," Marketing Science, INFORMS, vol. 3(2), pages 113-124.
- Jeffrey H. Horen, 1980. "Scheduling of Network Television Programs," Management Science, INFORMS, vol. 26(4), pages 354-370, April.
- Nikolopoulos, K. & Goodwin, P. & Patelis, A. & Assimakopoulos, V., 2007. "Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches," European Journal of Operational Research, Elsevier, vol. 180(1), pages 354-368, July.
- Kelton, Christina M. L. & Schneider Stone, Linda G., 1998. "Optimal television schedules in alternative competitive environments," European Journal of Operational Research, Elsevier, vol. 104(3), pages 451-473, February.
- Danaher, Peter J. & Dagger, Tracey S. & Smith, Michael S., 2011. "Forecasting television ratings," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1215-1240, October.
- Givon, Moshe & Grosfeld-Nir, Abraham, 2008. "Using partially observed Markov processes to select optimal termination time of TV shows," Omega, Elsevier, vol. 36(3), pages 477-485, June.
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