Forecasting aggregate retail sales : the case of South Africa

Show simple item record Aye, Goodness Chioma Balcilar, Mehmet Gupta, Rangan Majumdar, Anandamayee 2015-01-30T08:59:46Z 2015-01-30T08:59:46Z 2015-02
dc.description.abstract Forecasting 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.librarian hb2015 en_ZA
dc.description.uri en_ZA
dc.identifier.citation Aye, 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.issn 0925-5273 (print)
dc.identifier.issn 1873-7579 (online)
dc.identifier.other 10.1016/j.ijpe.2014.09.033
dc.language.iso en en_ZA
dc.publisher Elsevier en_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.subject Seasonality en_ZA
dc.subject Weightedloss en_ZA
dc.subject Retailsalesforecasting en_ZA
dc.subject Combination forecasts en_ZA
dc.subject South Africa (SA) en_ZA
dc.title Forecasting aggregate retail sales : the case of South Africa en_ZA
dc.type Postprint Article en_ZA

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