Date of this Version
Journal of Business Research
This article examines whether decomposing time series data into two parts – level and change – produces forecasts that are more accurate than those from forecasting the aggregate directly. Prior research found that, in general, decomposition reduced forecasting errors by 35%. An earlier study on decomposition into level and change found a forecast error reduction of 23%. The current study found that nowcasts consisting of a simple average of estimates from preliminary surveys and econometric models of the U.S. lodging market, improved the accuracy of final estimates of levels. Forecasts of change from an econometric model and the improved nowcasts reduced forecast errors by 29% when compared to direct forecasts of the aggregate. Forecasts of change from an extrapolation model and the improved nowcasts reduced forecast errors by 45%. On average then, the error reduction for this study was 37%.
Originally published in the Journal of Business Research © 2015 Elsevier
This is a pre-publication version. The final version is available at http://dx.doi.org/10.1016/j.jbusres.2015.03.035
accuracy, a priori analysis, conditional regression, nowcasting
Tessier, T. H., & Armstrong, J. S. (2015). Decomposition of Time-Series by Level and Change. Journal of Business Research, 68 (8), 1755-1758. http://dx.doi.org/10.1016/j.jbusres.2015.03.035
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Date Posted: 15 June 2018
This document has been peer reviewed.