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OIL PRICE SHOCKS, INVENTORIES, AND MACROECONOMIC DYNAMICS

Published online by Cambridge University Press:  30 January 2018

Ana María Herrera
Ana María Herrera*
Affiliation:
University of Kentucky
*
Address correspondence to: Ana María Herrera, Department of Economics, Gatton College of Business and Economics, Lexington, KY 40506-0034, USA; e-mail: [email protected].
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Abstract

This paper investigates the time delay in the transmission of oil price shocks using disaggregated manufacturing data on inventories and sales. VAR estimates indicate that industry-level inventories and sales respond faster to an oil price shock than aggregate gross domestic product, especially in industries that are energy-intensive. In response to an unexpected oil price increase, sales drop and inventories are accumulated. This leads to future reductions in production. We estimate a modified linear–quadratic inventory model to inquire whether the patterns observed in the VAR impulse responses are consistent with rational behavior by the firms. Estimation results suggest that three mechanisms play a role in the industry-level dynamics. First, oil prices act as a negative demand shock. Second, the shock catches manufacturers by surprise, resulting in higher-than-anticipated inventories. Third, because of their desire to smooth production, manufacturers deviate from the target level of inventories and spread the decline in production over various quarters; hence the delay in the response of aggregate output.

Keywords

Oil ShocksMacroeconomic FluctuationsInventories
Type
Articles
Information
Macroeconomic Dynamics , Volume 22 , Special Issue 3: Dynamics of Oil and Commodities Prices , April 2018 , pp. 620 - 639
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

This research was supported by the NSF under Grant SES-003840 and was partially completed while visiting Harvard's Kennedy School of Government under a Repsol-YPF research fellowship. I am thankful to Jim Hamilton, Bill Hogan, Lutz Kilian, Valerie Ramey, and three anonymous referees, as well as participants at numerous conferences and seminars, for helpful comments and suggestions.

References

REFERENCES

Akerlof, George A. (2002) Behavioral macroeconomics and macroeconomic behavior. American Economic Review 92 (3), 411–433.Google Scholar
Anderson, Evan W., Hansen, Lars P., McGrattan, Ellen R., and Sargent, Thomas J. (1996) Mechanics of forming and estimating dynamic linear economies. In Amman, H.M., Kendrick, D.A., and Rust, J. (eds.), Handbook of Computational Economics, Vol. 1, pp. 171–252. Amsterdam: North-Holland.Google Scholar
Blanchard, Olivier J. (1983) The production and inventory behavior of the American automobile industry. Journal of Political Economy 91, 365–400.Google Scholar
Christiano, Lawrence J., Eichenbaum, Martin, and Evans, Charles L. (2000) Monetary policy shocks: What have we learned and to what end? In Taylor, John B. and Woodford, Michael (eds.), Handbook of Macroeconomics Vol. 1, pp. 65–148. Amsterdam: North-Holland.Google Scholar
Christiano, Lawrence J., Eichenbaum, Martin, and Vigfusson, Robert J. (2003) What Happens after a Technology Shock? International finance discussion paper 768, Washington, DC: Board of Governors of the Federal Reserve System.Google Scholar
Durlauf, Steven N. and Maccini, Louis J. (1995) Measuring noise in inventory models. Journal of Monetary Economics 36, 65–89.Google Scholar
Eichenbaum, Martin S. (1989) Some empirical evidence on the production level and production cost smoothing models of inventory investment. American Economic Review 79, 853–864.Google Scholar
Fuhrer, Jeffrey C., Moore, George R., and Schuh, Scott D. (1995) Estimating the linear–quadratic inventory model: Maximum likelihood versus generalized method of moments. Journal of Monetary Economics 35, 115–157.Google Scholar
Hamilton, James D. (2002) On the interpretation of cointegration in the linear–quadratic inventory model. Journal of Economic Dynamics and Control 26, 2037–2049.CrossRefGoogle Scholar
Hamilton, James D. and Herrera, Ana María (2004) Oil shocks and aggregate economic behavior: The role of monetary policy. Journal of Money, Credit and Banking 36 (2), 265–286.Google Scholar
Holt, Charles C., Modigliani, Franco, Muth, John F., and Simon, Herbert A. (1960) Planning Production, Inventories, and Work Force. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Kashyap, Anil K. and Wilcox, David W. (1993) Production and inventory control at the General Motors Corporation during the 1920's and 1930's. American Economic Review 83, 383–401.Google Scholar
Kilian, Lutz (1998) Small-sample confidence intervals for impulse response functions. Review of Economics and Statistics 80 (2), 218–230.Google Scholar
Kilian, Lutz and Lewis, Logan T. (2011) Does the fed respond to oil price shocks? Economic Journal 121, 1047–1072.Google Scholar
Kilian, Lutz and Vega, Clara (2011) Do energy prices respond to U.S. macroeconomic news? A test of the hypothesis of predetermined energy prices. Review of Economics and Statistics 93 (2), 660–671.Google Scholar
Kollintzas, Tryphon (1995) A generalized variance bounds test with an application to the Holt et al. inventory model. Journal of Economic Dynamics and Control 19, 59–89.Google Scholar
Krane, Spencer D. and Braun, Steven N. (1991) Production-smoothing evidence from physical-product data. Journal of Political Economy 99, 558–577.Google Scholar
Lee, Kiseok and Ni, Shawn (2002) On the dynamic effects of oil price shocks: A study using industry level data. Journal of Monetary Economics 49, 823–852.Google Scholar
Mork, Knut A. (1989) Oil and the macroeconomy when prices go up and down: An extension of Hamilton's results. Journal of Political Economy 91, 740–744.Google Scholar
Ramey, Valerie A. (1991) Nonconvex costs and the behavior of inventories. Journal of Political Economy 99, 306–334.Google Scholar
Ramey, Valerie A. and West, Kenneth D. (1999) Inventories. In Taylor, John B. and Woodford, Michael (eds.), Handbook of Macroeconomics, Vol. IB, pp. 863–923. Amsterdam: North-Holland.Google Scholar
West, Kenneth D. (1983) A note on the econometric use of constant dollar inventory series. Economic Letters 13, 337–341.Google Scholar
West, Kenneth D. (1986) A variance bounds test of the linear quadratic inventory model. Journal of Political Economy 94, 374–401.Google Scholar
West., Kenneth D. (1995) Inventory models. In Pesaran, M. and Wickens, M. (eds.), Handbook of Applied Econometrics, Vol. I (Macroeconomics), pp. 188–220. Oxford, UK: Basil Blackwell.Google Scholar
West, Kenneth D. and Wilcox, David W. (1994) Estimation and inference in the linear–quadratic inventory model. Journal of Economic Dynamics and Control 18, 897–908.Google Scholar
West, Kenneth D. and Wilcox, David W. (1996) A comparison of alternative instrumental variables estimators of a dynamic linear model. Journal of Business and Economic Statistics 14, 281–293.Google Scholar
Whelan, Karl (2000) A Guide to the Use of Chain Aggregate NIPA Data. Federal Reserve Board of Governors finance and economics discussion series 2000–35.Google Scholar