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Forecasting N.S.W. Beef Production: An Evaluation of Alternative Techniques

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  • Gellatly, Colin

Abstract

This paper reports on the evaluation of the performance of several forecasting methods used to forecast New South Wales quarterly beef production, one quarter ahead. The forecasting procedures used are a single equation regression model, a Box-Jenkins univariate time series model, a forecasting committee's judgement and a naive model. Absolute accuracy and relative accuracy measures are used to evaluate ex ante forecasts. Although the evaluation gave some mixed results according to the criteria used, it appears that the forecasting committee performed better than the alternative forecasting procedures considered in this study. However, the results indicate that the committee's performance was not much better than that of a naive (no change) model, indicating there is room for improvement.

Suggested Citation

  • Gellatly, Colin, 1979. "Forecasting N.S.W. Beef Production: An Evaluation of Alternative Techniques," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 47(02), pages 1-14, August.
  • Handle: RePEc:ags:remaae:12478
    DOI: 10.22004/ag.econ.12478
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    References listed on IDEAS

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    1. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    2. N.C. Mackrell, 1974. "Techniques of Short Term Forecasting," RBA Research Discussion Papers rdp36, Reserve Bank of Australia.
    3. M. E. Burns, 1973. "A Note on the Choice of Data in Econometric Studies," The Economic Record, The Economic Society of Australia, vol. 49(1), pages 24-30, March.
    4. Raymond M. Leuthold, 1975. "On the Use of Theil's Inequality Coefficients," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 57(2), pages 344-346.
    5. repec:bla:ecorec:v:49:y:1973:i:125:p:24-30 is not listed on IDEAS
    6. Ryland, G.J., 1975. "Forecasting Crop Quality," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 43(02), pages 1-16, June.
    7. John W. Freebairn, 1975. "Forecasting For Australian Agriculture," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 19(3), pages 154-174, December.
    8. Mervin Daub, 1973. "On the Accuracy of Canadian Short-term Economic Forecasts," Canadian Journal of Economics, Canadian Economics Association, vol. 6(1), pages 90-107, February.
    9. Phoebus J. Dhrymes & E. Philip Howrey & Saul H. Hymans & Jan Kmenta & Edward E. Leamer & Richard E. Quandt & James B. Ramsey & Harold T. Shapiro & Victor Zarnowitz, 1972. "Criteria for Evaluation of Econometric Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 1, number 3, pages 291-324, National Bureau of Economic Research, Inc.
    10. Kym Anderson, 1974. "Distributed Lags And Barley Acreage Response Analysis," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 18(2), pages 119-132, August.
    11. M. N. Bhattacharyya, 1974. "Forecasting the Demand for Telephones in Australia," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 23(1), pages 1-10, March.
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    Keywords

    Livestock Production/Industries;

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