Forecasting Aggregate Retail Sales: The Case of South Africa
Goodness Aye (),
Mehmet Balcilar (),
Rangan Gupta () and
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Goodness Aye: Department of Economics, University of Pretoria
No 201312, Working Papers from University of Pretoria, Department of Economics
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.
Keywords: seasonality; weighted loss; retail sales forecasting; combination forecasts; South Africa (search for similar items in EconPapers)
JEL-codes: C32 C53 E32 (search for similar items in EconPapers)
Pages: 24 pages
New Economics Papers: this item is included in nep-afr and nep-for
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Journal Article: Forecasting aggregate retail sales: The case of South Africa (2015)
Working Paper: Forecasting Aggregate Retail Sales: The Case of South Africa (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:201312
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