Forecasting week-to-week television ratings using reduced-form and structural dynamic models

Rather than being sold several months before a program is aired, more than 20% of TV advertising slots are retained for sale weekly near the program’s broadcast time. Distinct from the literature that mainly focuses on the forecasting of program ratings for advanced sales of advertising slots, we explore approaches that can provide more accurate forecasts for near-real-time ratings. We propose two dynamic models that mainly employ individual viewing records for past episodes to forecast viewers’ decisions on episodes in the coming week, and therefore the ratings for these episodes. One is a reduced-form dynamic model that measures the influence of past watching experience by the weighted average of viewers’ choices of past episodes. The other is a structural dynamic model that goes deeper in its use of previous viewing information by modeling the underlying process of this influence based on Bayesian updating theory. Using data from the Hong Kong TV industry, we test and compare the two models. Results show that the reduced-form model generally performs better when the variance of ratings across episodes is small, while the structural model generates more accurate forecasts in other cases.