IDEAS home Printed from https://ideas.repec.org/p/tse/wpaper/31555.html
   My bibliography  Save this paper

Bayesian Estimation of the Storage Model using Information on Quantities

Author

Listed:
  • Gouel, Christophe
  • Legrand, Nicolas

Abstract

This paper presents a new strategy to estimate the rational expectations storage model. It uses information on prices and quantities – consumption and production – in contrast to previous approaches which use only prices. This additional information allows us to estimate a model with elastic supply, and to identify parameters such as supply and demand elasticities, which are left unidentified when using prices alone. The estimation relies on the Bayesian methods popularized in the literature on the estimation of DSGE models. It is carried out on a market representing the caloric aggregate of the four basic staples – maize, rice, soybeans, and wheat – from 1961 to 2006. The results show that to be consistent with the observed volatility of consumption, production, and price, elasticities have to be in the lower ranges of the elasticities in the literature, a result consistent with recent instrumental variable estimations on the same sample.

Suggested Citation

  • Gouel, Christophe & Legrand, Nicolas, 2017. "Bayesian Estimation of the Storage Model using Information on Quantities," TSE Working Papers 17-776, Toulouse School of Economics (TSE).
  • Handle: RePEc:tse:wpaper:31555
    as

    Download full text from publisher

    File URL: https://www.tse-fr.eu/sites/default/files/TSE/documents/doc/wp/2017/wp_tse_776.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Carlo Cafiero & Eugenio S.A. Bobenrieth H. & Juan R.A. Bobenrieth H. & Brian D. Wright, 2015. "Maximum Likelihood Estimation of the Standard Commodity Storage Model: Evidence from Sugar Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(1), pages 122-136.
    2. V. Ernesto Alex Guerra & Eugenio Sebastián Antonio Bobenrieth H. & Juan Rodrigo Andrés Bobenrieth H. & Carlo Cafiero, 2015. "Editor's choice Empirical commodity storage model: the challenge of matching data and theory," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 42(4), pages 607-623.
    3. Olivier Jean Blanchard & Stanley Fischer, 1990. "Editorial in "NBER Macroeconomics Annual 1990, Volume 5"," NBER Chapters, in: NBER Macroeconomics Annual 1990, Volume 5, pages 1-10, National Bureau of Economic Research, Inc.
    4. Dvir, Eyal & Rogoff, Kenneth, 2014. "Demand effects and speculation in oil markets: Theory and evidence," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 113-128.
    5. Angus Deaton & Guy Laroque, 1992. "On the Behaviour of Commodity Prices," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 59(1), pages 1-23.
    6. Olivier Jean Blanchard & Stanley Fischer, 1990. "NBER Macroeconomics Annual 1990, Volume 5," NBER Books, National Bureau of Economic Research, Inc, number blan90-1, March.
    7. Carroll, Christopher D., 2006. "The method of endogenous gridpoints for solving dynamic stochastic optimization problems," Economics Letters, Elsevier, vol. 91(3), pages 312-320, June.
    8. Michael K. Adjemian & Aaron Smith, 2012. "Using USDA Forecasts to Estimate the Price Flexibility of Demand for Agricultural Commodities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(4), pages 978-995.
    9. Harry Gorter & Dusan Drabik & David R. Just, 2015. "The Economics of Biofuel Policies," Palgrave Studies in Agricultural Economics and Food Policy, Palgrave Macmillan, number 978-1-137-41485-4, June.
    10. Michael J. Roberts & Wolfram Schlenker, 2013. "Identifying Supply and Demand Elasticities of Agricultural Commodities: Implications for the US Ethanol Mandate," American Economic Review, American Economic Association, vol. 103(6), pages 2265-2295, October.
    11. Brian Wright, 2014. "Global Biofuels: Key to the Puzzle of Grain Market Behavior," Journal of Economic Perspectives, American Economic Association, vol. 28(1), pages 73-98, Winter.
    12. World Bank & FAO, 2012. "The Grain Chain," World Bank Publications - Reports 23964, The World Bank Group.
    13. Anonymous, 1961. "International Monetary Fund," International Organization, Cambridge University Press, vol. 15(4), pages 710-712, October.
    14. Christophe Gouel & Nicolas Legrand, 2017. "Estimating the Competitive Storage Model with Trending Commodity Prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 744-763, June.
    15. Lucille Williamson & Paul Williamson, 1942. "What We Eat," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 24(3), pages 698-703.
    16. Robert J. Barro & Xavier Sala-i-Martin, 1990. "World Real Interest Rates," NBER Chapters, in: NBER Macroeconomics Annual 1990, Volume 5, pages 15-74, National Bureau of Economic Research, Inc.
    17. Anonymous, 1961. "International Monetary Fund," International Organization, Cambridge University Press, vol. 15(1), pages 194-195, January.
    18. Wright, Brian D & Williams, Jeffrey C, 1982. "The Economic Role of Commodity Storage," Economic Journal, Royal Economic Society, vol. 92(367), pages 596-614, September.
    19. Cafiero, Carlo & Bobenrieth H., Eugenio S.A. & Bobenrieth H., Juan R.A. & Wright, Brian D., 2011. "The empirical relevance of the competitive storage model," Journal of Econometrics, Elsevier, vol. 162(1), pages 44-54, May.
    20. Christophe Gouel, 2013. "Comparing Numerical Methods for Solving the Competitive Storage Model," Computational Economics, Springer;Society for Computational Economics, vol. 41(2), pages 267-295, February.
    21. Wright, Brian, 2014. "Global Biofuels: Key to the Puzzle of Grain Market Behavior," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt11715438, Department of Agricultural & Resource Economics, UC Berkeley.
    22. Deaton, Angus & Laroque, Guy, 1996. "Competitive Storage and Commodity Price Dynamics," Journal of Political Economy, University of Chicago Press, vol. 104(5), pages 896-923, October.
    23. Anonymous, 1961. "International Monetary Fund," International Organization, Cambridge University Press, vol. 15(3), pages 520-522, July.
    24. Gustafson, Robert L., 1958. "Carryover levels for grains: A method for determining amounts that are optimal under specified conditions," Technical Bulletins 157231, United States Department of Agriculture, Economic Research Service.
    25. Anonymous, 1961. "International Monetary Fund," International Organization, Cambridge University Press, vol. 15(2), pages 299-305, July.
    26. Olivier Jean Blanchard & Stanley Fischer (ed.), 1990. "NBER Macroeconomics Annual 1990," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262521555, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nicolas Legrand, 2019. "The Empirical Merit Of Structural Explanations Of Commodity Price Volatility: Review And Perspectives," Journal of Economic Surveys, Wiley Blackwell, vol. 33(2), pages 639-664, April.

    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. Nicolas Legrand, 2019. "The Empirical Merit Of Structural Explanations Of Commodity Price Volatility: Review And Perspectives," Journal of Economic Surveys, Wiley Blackwell, vol. 33(2), pages 639-664, April.
    2. Nicolas Legrand & Christophe Gouel, 2022. "The Role of Storage in Commodity Markets: Indirect Inference Based on Grains Data," Working Papers hal-03809825, HAL.
    3. Guerra Vallejos, Ernesto & Bobenrieth Hochfarber, Eugenio & Bobenrieth Hochfarber, Juan & Wright, Brian D., 2021. "Solving dynamic stochastic models with multiple occasionally binding constraints," Economic Modelling, Elsevier, vol. 105(C).
    4. V., Ernesto Guerra & H., Eugenio Bobenrieth & H., Juan Bobenrieth & Wright, Brian D., 2023. "Endogenous thresholds in energy prices: Modeling and empirical estimation," Energy Economics, Elsevier, vol. 121(C).
    5. Gabriela Simonet & Julie Subervie & Driss Ezzine-De-Blas & Marina Cromberg & Amy Duchelle, 2015. "Paying smallholders not to cut down the amazon forest: impact evaluation of a REDD+ pilot project," Working Papers 1514, Chaire Economie du climat.
    6. Christophe Gouel & Nicolas Legrand, 2017. "Estimating the Competitive Storage Model with Trending Commodity Prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 744-763, June.
    7. Jean‐Paul Chavas & Jian Li, 2020. "A quantile autoregression analysis of price volatility in agricultural markets," Agricultural Economics, International Association of Agricultural Economists, vol. 51(2), pages 273-289, March.
    8. Tran, A. Nam & Welch, Jarrod R. & Lobell, David & Roberts, Michael J. & Schlenker, Wolfram, 2012. "Commodity Prices and Volatility in Response to Anticipated Climate Change," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124827, Agricultural and Applied Economics Association.
    9. Eugenio S.A. Bobenrieth & Juan R.A. Bobenrieth & Ernesto A. Guerra & Brian D. Wright & Di Zeng, 2021. "Putting the Empirical Commodity Storage Model Back on Track: Crucial Implications of a “Negligible” Trend," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1034-1057, May.
    10. Juan R. A. Bobenrieth & Eugenio S. A. Bobenrieth & Andrés F. Villegas & Brian D. Wright, 2022. "Estimation of Endogenous Volatility Models with Exponential Trends," Mathematics, MDPI, vol. 10(15), pages 1-27, July.
    11. Kleppe, Tore Selland & Oglend, Atle, 2017. "Estimating the competitive storage model: A simulated likelihood approach," Econometrics and Statistics, Elsevier, vol. 4(C), pages 39-56.
    12. Chavas, Jean-Paul & Li, Jian, 2017. "The Effects of Private Stocks versus Public Stocks on Food Price Volatility," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 259185, Agricultural and Applied Economics Association.
    13. Fabio Gaetano Santeramo & Emilia Lamonaca & Francesco Contò & Gianluca Nardone & Antonio Stasi, 2018. "Drivers of grain price volatility: a cursory critical review," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 64(8), pages 347-356.
    14. Christophe Gouel, 2013. "Comparing Numerical Methods for Solving the Competitive Storage Model," Computational Economics, Springer;Society for Computational Economics, vol. 41(2), pages 267-295, February.
    15. Berg, Ernst, 2017. "Impacts of Inventory Management on Price Volatility in Agricultural Commodity Markets: Insights from a System Dynamics Model," 2017 International Congress, August 28-September 1, 2017, Parma, Italy 261281, European Association of Agricultural Economists.
    16. Roberts, Michael J. & Tran, A. Nam, 2013. "Conditional Suspension of the US Ethanol Mandate using Threshold Price inside a Competitive Storage Model," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150717, Agricultural and Applied Economics Association.
    17. Assia Elgouacem, 2018. "Essays on investment and saving [Essais sur l’investissement et l’épargne]," SciencePo Working papers Main tel-03419405, HAL.
    18. Roberts, Michael J. & Tran, A. Nam, 2012. "Commodity Price Adjustment in a Competitive Storage Model with an Application to the US Biofuel Policies," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124869, Agricultural and Applied Economics Association.
    19. Zhu, Xiaohong, 2016. "New models to estimate costs of US farm programs," ISU General Staff Papers 201601010800006209, Iowa State University, Department of Economics.
    20. Christophe Gouel, 2012. "Agricultural Price Instability: A Survey Of Competing Explanations And Remedies," Journal of Economic Surveys, Wiley Blackwell, vol. 26(1), pages 129-156, February.

    More about this item

    Keywords

    Commodity price dynamics; storage; Bayesian inference;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices

    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:tse:wpaper:31555. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/tsetofr.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.