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Forecasting aggregate retail sales: The case of South Africa

Author

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  • Aye, Goodness C.
  • Balcilar, Mehmet
  • Gupta, Rangan
  • Majumdar, Anandamayee

Abstract

Forecasting aggregate retail sales may improve portfolio investors׳ ability to predict movements in the stock prices of retail chains. 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 wide array of linear and nonlinear models, but also generate multi-step-ahead forecasts using a real-time recursive estimation scheme over the out-of-sample period, to better mimic the practical scenario faced by economic 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 the forecast error in booms and recessions. Focusing on the results of single models alone shows that their performances differ greatly across forecast horizons and for different weighting schemes, with no unique model performing the best across various scenarios. However, combination forecast models, especially the discounted mean-square forecast error method, which weighs current information more than past, not only produced better forecasts, but were also largely unaffected by business cycles and time horizons. This result, along with individual nonlinear models performing better than linear models, led us to conclude that theoretical research on retail sales should look at developing dynamic stochastic general equilibrium models that not only incorporate learning behavior, but also allow the behavioral parameters of the model to be state dependent, to account for regime-switching behavior across alternative states of the economy.

Suggested Citation

  • Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
  • Handle: RePEc:eee:proeco:v:160:y:2015:i:c:p:66-79
    DOI: 10.1016/j.ijpe.2014.09.033
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    Cited by:

    1. Chantal Rootman, 2016. "How social media tools influence brand image and buying behaviour in the South African food retail industry," Proceedings of Business and Management Conferences 3405542, International Institute of Social and Economic Sciences.
    2. repec:ipg:wpaper:2014-562 is not listed on IDEAS
    3. repec:ipg:wpaper:2014-475 is not listed on IDEAS
    4. Arunraj, Nari Sivanandam & Ahrens, Diane, 2015. "A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 321-335.

    More about this item

    Keywords

    Seasonality; Weighted loss; Retail sales forecasting; Combination forecasts; South Africa;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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