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An evaluation of price forecasts of the cattle market under structural changes

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  • Guney, Selin

Abstract

The specific purpose of this paper is to investigate the potential of a time series analysis technique, namely the Time Varying Parameter Vector Autoregressive Model (TVPVAR) technique, in the development of daily forecasting models for cattle prices in the presence of structural changes. More specific objectives are to integrate smoothing techniques and stochastic volatility into TVPAR modeling framework based exclusively on time series for cash-cattle prices, and to compare the accuracy and evaluate the forecasting performance of this model with the standard VAR model based on forecast accuracy measures.

Suggested Citation

  • Guney, Selin, 2015. "An evaluation of price forecasts of the cattle market under structural changes," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205109, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea15:205109
    DOI: 10.22004/ag.econ.205109
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    File URL: https://ageconsearch.umn.edu/record/205109/files/TVPVAR%20Shortened.pdf
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    References listed on IDEAS

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    1. Helmers, Glenn A. & Held, Larry J., 1977. "Comparison Of Livestock Price Forecasting Using Simple Techniques, Forward Pricing And Outlook Information," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 1(01), pages 1-4, June.
    2. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
    3. David A. Bessler & Ted Covey, 1991. "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 11(4), pages 461-474, August.
    4. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    5. Harald Uhlig, 1997. "Bayesian Vector Autoregressions with Stochastic Volatility," Econometrica, Econometric Society, vol. 65(1), pages 59-74, January.
    6. Yanhong H. Jin & Gabriel J. Power & Levan Elbakidze, 2008. "The Impact of North American BSE Events on Live Cattle Futures Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(5), pages 1279-1286.
    7. Tim W. Cogley, 2003. "Drifts and Volatilities: Monetary Policies and Outcomes in the Post War U.S," Working Papers 35, University of California, Davis, Department of Economics.
    8. Oliveira, Ronald A. & O'Connor, Carl W. & Smith, Gary W., 1979. "Short-Run Forecasting Models Of Beef Prices," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 4(01), pages 1-12, July.
    9. Christopher A. Sims, 1993. "A Nine-Variable Probabilistic Macroeconomic Forecasting Model," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 179-212, National Bureau of Economic Research, Inc.
    10. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    11. Engle, Robert F. & Yoo, Byung Sam, 1987. "Forecasting and testing in co-integrated systems," Journal of Econometrics, Elsevier, vol. 35(1), pages 143-159, May.
    12. Stein, Jerome L, 1981. "Speculative Price: Economic Welfare and the Idiot of Chance," The Review of Economics and Statistics, MIT Press, vol. 63(2), pages 223-232, May.
    13. Jeffrey H. Dorfman & Christopher S. McIntosh, 1990. "Results of a Price Forecasting Competition," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 72(3), pages 804-808.
    14. Gerlow, Mary E. & Irwin, Scott H. & Liu, Te-Ru, 1993. "Economic evaluation of commodity price forecasting models," International Journal of Forecasting, Elsevier, vol. 9(3), pages 387-397, November.
    15. Ronald L. Cooper, 1972. "The Predictive Performance of Quarterly Econometric Models of the United States," NBER Chapters, in: Econometric Models of Cyclical Behavior, Volumes 1 and 2, pages 813-947, National Bureau of Economic Research, Inc.
    16. James H. Stock & Mark W. Watson, 1993. "Business Cycles, Indicators, and Forecasting," NBER Books, National Bureau of Economic Research, Inc, number stoc93-1, February.
    17. Koop, Gary & Osiewalski, Jacek & Steel, Mark F J, 1994. "Posterior Properties of Long-Run Impulse Responses," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 489-492, October.
    18. Stock, James H. & Watson, Mark W. (ed.), 1993. "Business Cycles, Indicators, and Forecasting," National Bureau of Economic Research Books, University of Chicago Press, edition 1, number 9780226774886, June.
    19. Hansen, Bruce E., 2000. "Testing for structural change in conditional models," Journal of Econometrics, Elsevier, vol. 97(1), pages 93-115, July.
    20. Auer, Simone, 2019. "Monetary policy shocks and foreign investment income: Evidence from a large Bayesian VAR," Journal of International Money and Finance, Elsevier, vol. 93(C), pages 142-166.
    21. Nicholas E. Piggott & Thomas L. Marsh, 2004. "Does Food Safety Information Impact U.S. Meat Demand?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(1), pages 154-174.
    22. Glenn Nelson & Thomas Spreen, 1978. "Monthly Steer and Heifer Supply," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 60(1), pages 117-125.
    23. David Kenyon & Eluned Jones & M. Anya McGuirk, 1993. "Forecasting Performance of Corn and Soybean Harvest Futures Contracts," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 75(2), pages 399-407.
    24. Goodwin, Barry K., 1992. "Forecasting Cattle Prices in the Presence of Structural Change," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 24(2), pages 11-22, December.
    25. Bessler, David A. & Brandt, Jon A., 1992. "An analysis of forecasts of livestock prices," Journal of Economic Behavior & Organization, Elsevier, vol. 18(2), pages 249-263, July.
    26. Allen, P. Geoffrey, 1994. "Economic forecasting in agriculture," International Journal of Forecasting, Elsevier, vol. 10(1), pages 81-135, June.
    27. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    28. Sanders, Dwight R. & Manfredo, Mark R., 2003. "USDA Livestock Price Forecasts: A Comprehensive Evaluation," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 28(2), pages 1-19, August.
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    Cited by:

    1. Ikhoon, Jang & Young Chan, Choe, 2016. "Forecasting Agri-food Consumption Using the Keyword Volume Index from Search Engine Data," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236124, Agricultural and Applied Economics Association.

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    Keywords

    Agribusiness; Agricultural and Food Policy; Demand and Price Analysis; Farm Management; Livestock Production/Industries;
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