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Forecasting Aggregate Retail Sales: The Case of South Africa

Author

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  • Goodness C. Aye

    (Department of Economics, University of Pretoria Author-Email: aye_goody@yahoo.co.uk)

  • Mehmet Balcilar Author-Name-First Mehmet

    (Department of Economics, Eastern Mediterranean University, Famagusta, Northern Cyprus)

  • Rangan Gupta

    (Department of Economics, University of Pretoria)

  • Anandamayee Majumdar

    (Soochow University Center for Advance Statistics and Econometric Research, Suzhou, China)

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. We deviate from the uniform symmetric quadratic loss function typically used in forecast evaluation exercises. Hence, we consider loss functions that overweight forecast error in booms and recessions to check whether a specific model that appears to be a good choice on average is also preferable in times of economic stress. To this end, we use the weighted RMSE and weighted version of the Diebold-Mariano tests to evaluate the different forecasts. Focussing on the single models alone, results show that their performances differ greatly across forecast horizons and for different weighting schemes. However, the combination forecasts models in general produced better forecasts and are largely unaffected by business cycles and time horizons.

Suggested Citation

  • Goodness C. Aye & Mehmet Balcilar Author-Name-First Mehmet & Rangan Gupta & Anandamayee Majumdar, 2014. "Forecasting Aggregate Retail Sales: The Case of South Africa," Working Papers 15-21, Eastern Mediterranean University, Department of Economics.
  • Handle: RePEc:emu:wpaper:15-21.pdf
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    2. Tran Tuan Anh, 2015. "Assessing the development potential of modern retail in Vietnam," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ECONOMICS AND BUSINESS ADMINISTRATION, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 5(1), pages 31-40.
    3. Emir Zunic & Kemal Korjenic & Kerim Hodzic & Dzenana Donko, 2020. "Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data," Papers 2005.07575, arXiv.org.
    4. 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.
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    8. Kourentzes, Nikolaos & Barrow, Devon & Petropoulos, Fotios, 2019. "Another look at forecast selection and combination: Evidence from forecast pooling," International Journal of Production Economics, Elsevier, vol. 209(C), pages 226-235.
    9. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    10. Melchior, Cristiane & Zanini, Roselaine Ruviaro & Guerra, Renata Rojas & Rockenbach, Dinei A., 2021. "Forecasting Brazilian mortality rates due to occupational accidents using autoregressive moving average approaches," International Journal of Forecasting, Elsevier, vol. 37(2), pages 825-837.
    11. Erjiang E & Ming Yu & Xin Tian & Ye Tao, 2022. "Dynamic Model Selection Based on Demand Pattern Classification in Retail Sales Forecasting," Mathematics, MDPI, vol. 10(17), pages 1-16, September.
    12. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
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    15. 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.

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    More about this item

    Keywords

    seasonality; weighted loss; retail sales forecasting; combination forecasts; South Africa;
    All these keywords.

    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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