A Comparison of Seasonal Regression Forecasting Models for the U.S. Beer Import Market

Abstract

Demand seasonality in the U.S. Imported Beer industry is common. The financial cycles of the past decade brought some extreme fluctuations to industry demand, which was trending upward. This research extends previous work in this area by comparing seasonal forecasting models for two time periods: 1999–2007 and 1999–2012. The previous study (Kros & Keller, 2010) examined the 1999–2007 time frame while this study extends their model using the new data. Models are developed within Excel and include a simple yearly model, a semi-annual model, a quarterly model, and a monthly model. The results of the models are compared and a discussion of each model’s efficacy is provided. While, the models did do a good job forecasting U.S. Import Beer sales from 1999 to 2007 the economic downturn starting in 2007 was deleterious to some models continued efficacy. When the data from the downturn is accounted for it is concluded that the seasonal models presented are doing an overall good job of forecasting U.S. Import Beer Sales and assisting managers in shorter time frame forecasting.

Citation

Kros, J.F., Rowe, W.J. and Brown, E.C. (2014), "A Comparison of Seasonal Regression Forecasting Models for the U.S. Beer Import Market", Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 10), Emerald Group Publishing Limited, Leeds, pp. 161-177. https://doi.org/10.1108/S1477-407020140000010021