| By: |
Denny Meyer;
Rob J. Hyndman |
| Abstract: |
This paper investigates the effect of aggregation and non-linearity in
relation to television rating forecasts. Several linear models for aggregated
and disaggregated television viewing have appeared in the literature. The
current analysis extends this work using an empirical approach. We compare the
accuracy of population rating models, segment rating models and individual
viewing behaviour models. Linear and non-linear models are fitted using
regression, decision trees and neural networks, with a two-stage procedure
being used to model network choice and viewing time for the individual viewing
behaviour model. The most accurate forecast results are obtained from the
non-linear segment rating models. |
| Keywords: |
Decision Trees, Disaggregation, Discrete Choice Models, Neural Networks, Rating Benchmarks |
| JEL: |
C53 C51 C35 M37 |
| Date: |
2005–03 |
| URL: |
http://d.repec.org/n?u=RePEc:msh:ebswps:2005-1&r=cul |