Forecasting Aggregate Retail Sales: The Case of South Africa
AbstractForecasting 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.
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Bibliographic InfoPaper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201312.
Length: 24 pages
Date of creation: Feb 2013
Date of revision:
seasonality; weighted loss; retail sales forecasting; combination forecasts; South Africa;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect 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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