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An Empirical Model of Inventory Investment by Durable Commodity Intermediaries

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Abstract

This paper introduces a new detailed data set of high-frequency observations on inventory investment by a U.S. steel wholesaler. Our analysis of these data leads to six main conclusions: orders and sales are made infrequently; orders are more volatile than sales; order sizes vary considerably; there is substantial high-frequency variation in the firm's sales prices; inventory/sales ratios are unstable; and there are occasional stockouts. We model the firm generically as a durable commodity intermediary that engages in commodity price speculation. We demonstrate that the firm's inventory investment behavior at the product level is well approximated by an optimal trading strategy from the solution to a nonlinear dynamic programming problem with two continuous state variables and one continuous control variable that is subject to frequently binding inequality constraints. We show that the optimal trading strategy is a generalized (S,s) rule. That is, whenever the firm's inventory level q falls below the order threshold s(p) the firm places an order of size S(p) - q in order to attain a target inventory level S(p) satisfying S(p) >= s(p), where p is the current spot price at which the firm can purchase unlimited amounts of the commodity after incurring a fixed order cost K. We show that the (S,s) bands are decreasing functions of p, capturing the basic intuition of commodity price speculation, namely, that it is optimal for the firm to hold higher inventories when the spot price is low than when it is high in order to profit from "buying low and selling high." We simulate a calibrated version of this model and show that the simulated data exhibit the key features of inventory investment we observe in the data.

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  • George J. Hall & John Rust, 1999. "An Empirical Model of Inventory Investment by Durable Commodity Intermediaries," Cowles Foundation Discussion Papers 1228, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:1228
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    Cited by:

    1. ARATA Yoshiyuki, 2015. "Endogenous Business Cycles Caused by Nonconvex Costs and Interactions," Discussion papers 15085, Research Institute of Economy, Trade and Industry (RIETI).
    2. Alessandro Gavazza, 2016. "An Empirical Equilibrium Model of a Decentralized Asset Market," Econometrica, Econometric Society, vol. 84, pages 1755-1798, September.
    3. George Hall and John Rust, Yale University, 2001. "Econometric Methods for Endogenously Sampled Time Series: The Case of Commodity Price Speculation in the Steel Market," Computing in Economics and Finance 2001 274, Society for Computational Economics.
    4. George Alessandria & Joseph P. Kaboski & Virgiliu Midrigan, 2010. "Inventories, Lumpy Trade, and Large Devaluations," American Economic Review, American Economic Association, vol. 100(5), pages 2304-2339, December.
    5. Gautier, Erwan & Le Bihan, Hervé, 2011. "Time-varying (S, s) band models: Properties and interpretation," Journal of Economic Dynamics and Control, Elsevier, vol. 35(3), pages 394-412, March.
    6. Gautier, E. & Le Bihan, H., 2009. "Time-varying (S, s) band models: empirical properties and interpretation," Working papers 231, Banque de France.
    7. Aubhik Khan & Julia K. Thomas, 2007. "Inventories and the Business Cycle: An Equilibrium Analysis of ( S , s ) Policies," American Economic Review, American Economic Association, vol. 97(4), pages 1165-1188, September.
    8. Fabio Antoniou & Raffaele Fiocco, 2019. "Strategic inventories under limited commitment," RAND Journal of Economics, RAND Corporation, vol. 50(3), pages 695-729, September.
    9. Victor Aguirregabiria & Francis Guiton, 2022. "Decentralized Decision-Making in Retail Chains: Evidence from Inventory Management," Working Papers tecipa-722, University of Toronto, Department of Economics.
    10. Hall, George & Rust, John, 2021. "Estimation of endogenously sampled time series: The case of commodity price speculation in the steel market," Journal of Econometrics, Elsevier, vol. 222(1), pages 219-243.
    11. Florian Zettelmeyer & Fiona Scott Morton & Jorge Silva-Risso, 2006. "Scarcity Rents in Car Retailing: Evidence from Inventory Fluctuations at Dealerships," NBER Working Papers 12177, National Bureau of Economic Research, Inc.
    12. Ayelet Israeli & Fiona Scott-Morton & Jorge Silva-Risso & Florian Zettelmeyer, 2022. "How Market Power Affects Dynamic Pricing: Evidence from Inventory Fluctuations at Car Dealerships," Management Science, INFORMS, vol. 68(2), pages 895-916, February.
    13. Oleksiy Kryvtsov & Virgiliu Midrigan, 2013. "Inventories, Markups, and Real Rigidities in Menu Cost Models," Review of Economic Studies, Oxford University Press, vol. 80(1), pages 249-276.
    14. Blume, Lawrence E. & Easley, David & Kleinberg, Jon & Tardos, Éva, 2009. "Trading networks with price-setting agents," Games and Economic Behavior, Elsevier, vol. 67(1), pages 36-50, September.
    15. Robert L. Bray & Haim Mendelson, 2015. "Production Smoothing and the Bullwhip Effect," Manufacturing & Service Operations Management, INFORMS, vol. 17(2), pages 208-220, May.
    16. John Rust & George Hall, 2003. "Middlemen versus Market Makers: A Theory of Competitive Exchange," Journal of Political Economy, University of Chicago Press, vol. 111(2), pages 353-403, April.
    17. Horst Raff & Nicolas Schmitt & Frank Stähler, 2018. "How Importers May Hedge Demand Uncertainty," Discussion Papers dp18-03, Department of Economics, Simon Fraser University.
    18. Ejarque, João Miguel, 2011. "Evaluating the economic cost of natural gas strategic storage restrictions," Energy Economics, Elsevier, vol. 33(1), pages 44-55, January.
    19. Jia Yan & John Liu, 2008. "Instability of Dynamic Inventory Systems," Working Papers 2008-23, School of Economic Sciences, Washington State University.
    20. Robert L. Bray & Yuliang Yao & Yongrui Duan & Jiazhen Huo, 2019. "Ration Gaming and the Bullwhip Effect," Operations Research, INFORMS, vol. 67(2), pages 453-467, March.
    21. Yoshiyuki Arata, 2017. "Endogenous business cycles caused by nonconvex costs and interactions," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(2), pages 367-391, July.
    22. Chris Muris & Horst Raff & Nicolas Schmitt & Frank Stähler, 2023. "Inventory, Sourcing, and the Effects of Trade Costs: Theory and Empirical Evidence," CESifo Working Paper Series 10253, CESifo.
    23. Ortner, Juan, 2017. "Durable goods monopoly with stochastic costs," Theoretical Economics, Econometric Society, vol. 12(2), May.

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    More about this item

    Keywords

    Commodities; inventories; dynamic programming;
    All these keywords.

    JEL classification:

    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity

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