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The choice of a forecasting model

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  • Fildes, Robert
  • Lusk, Edward J

Abstract

The major purpose of studies of forecasting accuracy is to help forecasters select the 'best' forecasting method. This paper examines accuracy studies in particular that of Makridakis et al. [20] with a view to establishing how they contribute to model choice. It is concluded that they affect the screening that most forecasters go through in selecting a range of methods to analyze--in Bayesian terms they are a major determinant of 'prior knowledge'. This general conclusion is illustrated in the specific case of the Makridakis Competition (M-Competition). A survey of expert forecasters was made in both the UK and US. The respondents were asked about their familiarity with eight methods of univariate time series forecasting, and their perceived accuracy in three different forecasting situations. The results, similar for both the UK and US, were that the forecasters were relatively familiar with all the techniques included except Holt-Winters and Bayesian. For short horizons Box-Jenkins was seen as most accurate while trend curves was perceived as most suitable for the long horizons. These results are contrasted with those of the M-Competition, and conclusions drawn on how the results of the M-Competition should influence model screening and model choice.

Suggested Citation

  • Fildes, Robert & Lusk, Edward J, 1984. "The choice of a forecasting model," Omega, Elsevier, vol. 12(5), pages 427-435.
  • Handle: RePEc:eee:jomega:v:12:y:1984:i:5:p:427-435
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    Cited by:

    1. Weatherford, Larry R. & Kimes, Sheryl E., 2003. "A comparison of forecasting methods for hotel revenue management," International Journal of Forecasting, Elsevier, vol. 19(3), pages 401-415.
    2. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
    3. Fred Collopy & J. Scott Armstrong, 1992. "Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations," Management Science, INFORMS, vol. 38(10), pages 1394-1414, October.
    4. Winklhofer, Heidi & Diamantopoulos, Adamantios & Witt, Stephen F., 1996. "Forecasting practice: A review of the empirical literature and an agenda for future research," International Journal of Forecasting, Elsevier, vol. 12(2), pages 193-221, June.
    5. Bera, Soumitra Kumar, 2010. "Forecasting model of small scale industrial sector of West Bengal," MPRA Paper 28144, University Library of Munich, Germany.
    6. Tang, Hui-Wen Vivian & Yin, Mu-Shang, 2012. "Forecasting performance of grey prediction for education expenditure and school enrollment," Economics of Education Review, Elsevier, vol. 31(4), pages 452-462.

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