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Enabling Users to Evaluate the Accuracy of ABARES Agricultural Forecasts

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  • Cameron, Andrew
  • Nelson, Rohan

Abstract

This paper describes the development of an online database which allows users to assess the accuracy of ABARES agricultural market forecasts for around 100 variables over nearly two decades. Accuracy underpins the broader quality dimensions of ABARES forecasts such as institutional alignment and value-in-use to end users. The accuracy of ABARES forecasts generally improves as the lead time between forecast and outcome reduces, and production forecasts are slightly more accurate than corresponding price or export forecasts. Overall results show that ABARES forecasts are generally unbiased, but that bias can be a transient issue in markets undergoing structural change. The ability to analyse accuracy at low cost is a foundational step towards future research into the value of ABARES forecasts for supporting decision making.

Suggested Citation

  • Cameron, Andrew & Nelson, Rohan, 2022. "Enabling Users to Evaluate the Accuracy of ABARES Agricultural Forecasts," Australasian Agribusiness Review, University of Melbourne, Department of Agriculture and Food Systems, vol. 30(7), November.
    • Handle: RePEc:ags:auagre:335272
    DOI: 10.22004/ag.econ.335272
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    References listed on IDEAS

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    1. 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.
    2. Green, Kesten C. & Armstrong, J. Scott & Graefe, Andreas, 2015. "Golden rule of forecasting rearticulated: Forecast unto others as you would have them forecast unto you," Journal of Business Research, Elsevier, vol. 68(8), pages 1768-1771.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. Abarbanell, Jeffery & Lehavy, Reuven, 2003. "Biased forecasts or biased earnings? The role of reported earnings in explaining apparent bias and over/underreaction in analysts' earnings forecasts," Journal of Accounting and Economics, Elsevier, vol. 36(1-3), pages 105-146, December.
    5. O.T. Kingma & James L. Longmire & Andrew B. Stoeckel, 1980. "A Review Of Three Research Programs In Quantitative Modelling In The Bureau Of Agricultural Economics," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 24(3), pages 224-247, December.
    6. Freebairn, John W., 1978. "An Evaluation of Outlook Information for Australian Agricultural Commodities," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 46(03), pages 1-21, December.
    7. Allen, P. Geoffrey, 1994. "Economic forecasting in agriculture," International Journal of Forecasting, Elsevier, vol. 10(1), pages 81-135, June.
    8. Kingma, Onko Tjerek & Longmire, James L. & Stoeckel, Andrew B., 1980. "A Review Of Three Research Programs In Quantitative Modelling In The Bureau Of Agricultural Economics," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 24(3), pages 1-24, December.
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    Cited by:

    1. Nelson, Rohan & Cameron, Andrew & Xia, Charley & Gooday, Peter, 2022. "The ABARES Approach to Forecasting Agricultural Commodity Markets," Australasian Agribusiness Review, University of Melbourne, Department of Agriculture and Food Systems, vol. 30(6), November.

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