Forecasting aggregate retail sales : the case of South Africa

Loading...
Thumbnail Image

Authors

Aye, Goodness Chioma
Balcilar, Mehmet
Gupta, Rangan
Majumdar, Anandamayee

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

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.

Description

Keywords

Seasonality, Weightedloss, Retailsalesforecasting, Combination forecasts, South Africa (SA)

Sustainable Development Goals

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.