Open Access

Günter Lang

• Over more than a decade, advertising rates per 1000 viewers, television consumption as well as the number of advertising spots have been steadily increasing. As a consequence, television has developed to the most important medium for the advertising industry and attracts a 40% share of German gross advertising spending. Motivated by the recent slump of advertising rates and of the number of spots, this paper attempts to develop a forecast model for real advertising spending on the German TV market. In a first step, spectral analysis is used to identify the most important cycles on the advertising market. In a second step, the identified cycles are entering a regression model which is the basis for making forecasts. To evaluate the forecast quality, the results are compared to a standard ARIMA model. The estimations are based on monthly data of the German TV advertising market from 1990 to 2004. Actually, the results show that the estimated cyclical pattern describes the trends on theOver more than a decade, advertising rates per 1000 viewers, television consumption as well as the number of advertising spots have been steadily increasing. As a consequence, television has developed to the most important medium for the advertising industry and attracts a 40% share of German gross advertising spending. Motivated by the recent slump of advertising rates and of the number of spots, this paper attempts to develop a forecast model for real advertising spending on the German TV market. In a first step, spectral analysis is used to identify the most important cycles on the advertising market. In a second step, the identified cycles are entering a regression model which is the basis for making forecasts. To evaluate the forecast quality, the results are compared to a standard ARIMA model. The estimations are based on monthly data of the German TV advertising market from 1990 to 2004. Actually, the results show that the estimated cyclical pattern describes the trends on the TV advertising market very well. The cycle model is therefore a useful tool for making ex-ante forecasts of real advertising spending. Underlining the quality of the approach, the ARIMA approach is performing significantly worse than the introduced cycle model.…