Forecasting aggregate retail sales : the case of South Africa

dc.contributor.authorAye, Goodness Chioma
dc.contributor.authorBalcilar, Mehmet
dc.contributor.authorGupta, Rangan
dc.contributor.authorMajumdar, Anandamayee
dc.contributor.emailrangan.gupta@up.ac.zaen_ZA
dc.date.accessioned2015-01-30T08:59:46Z
dc.date.available2015-01-30T08:59:46Z
dc.date.issued2015-02
dc.description.abstractForecasting aggregate retail sales may improve portfolio investors‟ ability to predict movements in the stock prices of the retailing chains. Therefore, this paper uses 26 (23 single and 3 combination) forecasting models to forecast South Africa‟s aggregate seasonal retail sales. We use data from 1970:01 – 2012:05, with 1987:01-2012:05 as the out-of-sample period. Unlike, the previous literature on retail sales forecasting, we not only look at a wider array of linear and nonlinear models, but also generate multi-steps-ahead forecasts using a real-time recursive estimation scheme over the out-of-sample period, to mimic better the practical scenario faced by agents making retailing decisions. In addition, we deviate from the uniform symmetric quadratic loss function typically used in forecast evaluation exercises, by considering loss functions that overweight forecast error in booms and recessions. Focusing on the single models alone, results show that their performances differ greatly across forecast horizons and for different weighting schemes, with no unique model performing the best across various scenarios. However, the combination forecasts models, especially the discounted mean-square forecast error method which weighs current information more than past, produced not only better forecasts, but were also largely unaffected by business cycles and time horizons. This result, along with the fact that individual nonlinear models performed better than linear models, led us to conclude that theoretical research on retail sales should look at developing dynamic stochastic general equilibrium models which not only incorporates learning behaviour, but also allows the behavioural parameters of the model to be state-dependent, to account for regime-switching behaviour across alternative states of the economy.en_ZA
dc.description.librarianhb2015en_ZA
dc.description.urihttp://www.elsevier.com/locate/ijpeen_ZA
dc.identifier.citationAye, GC, Balcilar, M, Gupta, R & Majumdar, A 2015, 'Forecasting aggregate retail sales : the case of South Africa', International Journal of Production Economics, vol. 160, pp. 66-79.en_ZA
dc.identifier.issn0925-5273 (print)
dc.identifier.issn1873-7579 (online)
dc.identifier.other10.1016/j.ijpe.2014.09.033
dc.identifier.urihttp://hdl.handle.net/2263/43511
dc.language.isoenen_ZA
dc.publisherElsevieren_ZA
dc.rights© 2014 Elsevier B.V. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in International Journal of Production Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Production Economics, vol.160, pp. 66-79, 2015. doi :10.1016/j.ijpe.2014.09.033.en_ZA
dc.subjectSeasonalityen_ZA
dc.subjectWeightedlossen_ZA
dc.subjectRetailsalesforecastingen_ZA
dc.subjectCombination forecastsen_ZA
dc.subjectSouth Africa (SA)en_ZA
dc.titleForecasting aggregate retail sales : the case of South Africaen_ZA
dc.typePostprint Articleen_ZA

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Aye_Forecasting_2015.pdf
Size:
555.66 KB
Format:
Adobe Portable Document Format
Description:
Postprint Article

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: