Marketing Papers

Document Type

Technical Report

Date of this Version

8-2015

Publication Source

Journal of Business Research

Volume

68

Issue

8

Start Page

1755

Last Page

1758

DOI

10.1016/j.jbusres.2015.03.035

Abstract

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%.

Copyright/Permission Statement

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

Keywords

accuracy, a priori analysis, conditional regression, nowcasting

Embargo Date

9-1-2018

Available for download on Saturday, September 01, 2018

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Date Posted: 15 June 2018

This document has been peer reviewed.