IDEAS home Printed from https://ideas.repec.org/p/ags/aaea15/205740.html
   My bibliography  Save this paper

Efficiency Gains in Commodity Forecasting with High Volatility in Prices using Different Levels of Data Aggregation

Author

Listed:
  • Pena-Levano, Luis M.
  • Ramirez, Octavio
  • Renteria-Pinon, Mario

Abstract

This study evaluates the efficiency gains in forecasting three commodity prices (live cattle, coffee and cotton) using time series models. Different leves of temporal aggregations are tested (weekly, monthly, quarterly and annually). The objective is to test whether models based on disaggregated data can produce better price forecasting than the corresponding model using a higher level of aggregation. For example, we test if weekly models can predict better monthly prices than monthly models. Because the high volatility in real prices, we evaluate the possible non-stationarity behavior and heteroskedasticity of each commodity. Then, we use time series methods to model the prices and select the best estimators at each aggregation level and commodity. For the three commodity prices, models based on disaggregated levels effectively provided an efficiency gain in forecasting. Among these levels, the best models were the weekly models. The same behavior was consistent across all possible levels of aggregations.

Suggested Citation

  • Pena-Levano, Luis M. & Ramirez, Octavio & Renteria-Pinon, Mario, 2015. "Efficiency Gains in Commodity Forecasting with High Volatility in Prices using Different Levels of Data Aggregation," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205740, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea15:205740
    DOI: 10.22004/ag.econ.205740
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/205740/files/AAEA%202015%20Paper%20-%20%20Efficiency%20Gains%20in%20Commodity%20Forecasting%20with%20High%20Volatility%20in%20Prices%20using%20Different%20Levels%20of%20Data%20Aggregation.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.205740?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Pena-Levano, Luis M. & Ramirez, Octavio A., 2014. "Efficiency Gains In Cotton Price Forecasting Using Different Levels Of Data Aggregation," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169814, Agricultural and Applied Economics Association.
    2. Barry A. Goss & S. Gulay Avsar, 1999. "Non‐storables, Simultaneity and Price Determination: The Australian (Finished) Live Cattle Market," Australian Economic Papers, Wiley Blackwell, vol. 38(4), pages 461-480, December.
    3. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    4. David E. Kenyon, 2001. "Producer Ability to Forecast Harvest Corn and Soybean Prices," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 23(1), pages 151-162.
    5. James S. Eales & Brian K. Engel & Robert J. Hauser & Sarahelen R. Thompson, 1990. "Grain Price Expectations of Illinois Farmers and Grain Merchandisers," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 72(3), pages 701-708.
    6. Nijman, Theo E & Palm, Franz C, 1990. "Predictive Accuracy Gain from Disaggregate Sampling in ARIMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(4), pages 405-415, October.
    7. Masahiro Kawai, 1983. "Spot and Futures Prices of Nonstorable Commodities Under Rational Expectations," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 98(2), pages 235-254.
    8. Unknown, 2012. "CFCS 48th Annual Meeting," 48th Annual Meeting, May 20-26th, 2012, Playa del Carmen, Mexico 253586, Caribbean Food Crops Society.
    9. Raymond M. Leuthold & Peter A. Hartmann, 1979. "A Semi-Strong Form Evaluation of the Efficiency of the Hog Futures Market," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 61(3), pages 482-489.
    10. Ole Gjølberg & Berth-Arne Bengtsson, 1997. "Forecasting quarterly hog prices: Simple autoregressive models vs. naive predictions," Agribusiness, John Wiley & Sons, Ltd., vol. 13(6), pages 673-679.
    11. Dhuyvetter, Kevin C. & Swanser, Kole & Kastens, Terry L. & Mintert, James R. & Crosby, Brett, 2008. "Improving Feeder Cattle Basis Forecasts," 2008 Annual Meeting, June 23-24, 2008, Big Sky, Montana 42302, Western Agricultural Economics Association.
    12. Ramirez, Octavio A., 2011. "Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts," Faculty Series 113520, University of Georgia, Department of Agricultural and Applied Economics.
    13. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    14. B. S. Fisher & Carolyn Tanner, 1978. "The Formulation of Price Expectations: An Empirical Test of Theoretical Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 60(2), pages 245-248.
    15. Tonsor, Glynn T. & Dhuyvetter, Kevin C. & Mintert, James R., 2004. "Improving Cattle Basis Forecasting," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 29(2), pages 1-14, August.
    16. David E. A. Giles & Barry A. Goss & Olive P. L. Chin, 1985. "Intertemporal Allocation in the Corn and Soybean Markets with Rational Expectations," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 67(4), pages 749-760.
    17. Yue Fang & Sergio G. Koreisha, 2004. "Updating ARMA predictions for temporal aggregates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(4), pages 275-296.
    18. repec:bla:ausecp:v:38:y:1999:i:4:p:461-80 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pena-Levano, Luis M & Foster, Kenneth, 2016. "Efficiency gains in commodity forecasting using disaggregated levels versus more aggregated predictions," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235792, Agricultural and Applied Economics Association.
    2. Barry A. Goss & S. Gulay Avsar & Siang‐Choo Chan, 1992. "Rational Expectations and Price Determination in the US Oats Market," The Economic Record, The Economic Society of Australia, vol. 68(S1), pages 16-26, December.
    3. Sherrick, Bruce J., 2002. "The Accuracy Of Producers' Probability Beliefs: Evidence And Implications For Insurance Valuation," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 27(1), pages 1-17, July.
    4. SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," LIDAM Discussion Papers CORE 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Burton, Diana M. & Love, H. Alan, 1996. "A Review of Alternative Expectations Regimes in Commodity Markets: Specification, Estimation, and Hypothesis Testing Using Structural Models," Agricultural and Resource Economics Review, Cambridge University Press, vol. 25(2), pages 213-231, October.
    6. Sanders, Dwight R. & Garcia, Philip & Manfredo, Mark R., 2008. "Information Content in Deferred Futures Prices: Live Cattle and Hogs," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 33(1), pages 1-12, April.
    7. Blank, Steven C., 1989. "Research On Futures Markets: Issues, Approaches, And Empirical Findings," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 14(1), pages 1-14, July.
    8. Ramirez, Octavio A., 2011. "Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts," Faculty Series 113520, University of Georgia, Department of Agricultural and Applied Economics.
    9. Yoon, Byung-Sam & Brorsen, B. Wade, 2002. "Market Inversion in Commodity Futures Prices," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 34(3), pages 459-476, December.
    10. Kastens, Terry L. & Schroeder, Ted C., 1993. "Expectations of Cattle Feeding Investors in Feeder Cattle Placements," Staff Papers 118159, Kansas State University, Department of Agricultural Economics.
    11. Pudenz, Christopher C. & Schulz, Lee L., 2021. "Packer Procurement, Structural Change, and Moving Average Basis Forecasts: Lessons from the Fed Dairy Cattle Industry," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 46(3), September.
    12. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
    13. Björn Kauder & Niklas Potrafke & Christoph Schinke, 2017. "Manipulating Fiscal Forecasts: Evidence from the German States," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 73(2), pages 213-236, June.
    14. Sherrick, Bruce J., 2001. "The Accuracy Of Producer Expectations: Evidence And Implications For Insurance Valuation," 2001 Regional Committee NC-221, October 1-2, 2001, McLean, Virginia 132390, Regional Research Committee NC-1014: Agricultural and Rural Finance Markets in Transition.
    15. Kastens, Terry L. & Schroeder, Ted C., 1994. "Cattle Feeder Behavior And Feeder Cattle Placements," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 19(2), pages 1-12, December.
    16. Pedersen, Michael, 2019. "Anomalies in macroeconomic prediction errors–evidence from Chilean private forecasters," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1100-1107.
    17. Gene Birz & Sandip Dutta & Han Yu, 2022. "Economic forecasts, anchoring bias, and stock returns," Financial Management, Financial Management Association International, vol. 51(1), pages 169-191, March.
    18. Andrea Silvestrini & Matteo Salto & Laurent Moulin & David Veredas, 2008. "Monitoring and forecasting annual public deficit every month: the case of France," Empirical Economics, Springer, vol. 34(3), pages 493-524, June.
    19. Moledina, Amyaz A. & Roe, Terry L. & Shane, Mathew, 2004. "Measuring Commodity Price Volatility And The Welfare Consequences Of Eliminating Volatility," 2004 Annual meeting, August 1-4, Denver, CO 19963, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    20. Rik Hafer & Scott E. Hein, 1989. "Comparing futures and survey forecasts of near-term Treasury bill rates," Review, Federal Reserve Bank of St. Louis, issue May, pages 33-42.

    More about this item

    Keywords

    Agribusiness; Research Methods/ Statistical Methods; Risk and Uncertainty;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaea15:205740. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.