Influential Factors in the Econometric Modeling of the Price of Wheat in the United States of America

Fergus J. D. Keatinge

Department of Geography, University of Florida, Gainesville, USA.

DOI: 10.4236/as.2015.68073                

Abstract

Wheat is a staple agricultural grain commodity used within the United States and is grown in nearly every state. Modeling the price of Hard Winter Red wheat (the most common type of wheat) is of extreme economic and social importance. The 2008 financial crisis had a drastic effect on the price of food in real terms, tightening household budgets and increasing the US percentage of citizen classed below the poverty line. Understanding the influential factors in the econometric modeling of the price of wheat allows for more effective governmental intervention and price stabilization. Results indicate that the price of wheat is influenced by a combination of 5 separate functions: “supply”, “demand”, “macroeconomic”, “climate” and “natural resource” related functions. These functions derive from a wide variety of different data sources. The functions were determined and then incorporated into an Ordinary Least Squares (OLS) regression model taking into account variable interaction, variable transformation and time. This regression exercise resulted in a good model, explaining just over 90% of the variation in the price of wheat. Yet, results indicate that the model though sensitive to sharp decreases in the price of wheat is insensitive to sharp increases in the price of wheat. Ideas are discussed of ways of improving the price model. These include the addition of other variables, such as financial speculation/increased use of climate related variables and the idea of using alternative statistical modeling techniques in place of robust OLS regression modeling, such as SVAR models and Spline GARCH models. This research implies that further research into the modeling of the price of wheat within the US has useful potential for a more productive outcome.

Keywords

US Wheat Price, Regression, Supply and Demand, Crude Oil, ENSO, US Inflation

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Western Organization Resource Council (2002) United States Wheat Production. http://www.worc.org/userfiles/WORCproductionfactsheet.pdf
[2]	United States Department of Agriculture (2015) WHEAT DATA. http://www.ers.usda.gov/data-products/wheat-data.aspx
[3]	United States Department of Agriculture (2015) Grain: World Markets and Trade. Foreign Agricultural Service. http://apps.fas.usda.gov/psdonline/circulars/grain.pdf
[4]	Janzen, J.P., Carter, C.A., Smith, A.D. and Adjemian, M.K. (2014) Deconstructing Wheat Price Spikes: A Model of Supply and Demand, Financial Speculation, and Commodity Price Co-movement. United States Department of Agriculture—Economic Research Report. 1-41. http://dx.doi.org/10.2139/ssrn.2502922
[5]	Van Meir, L.W. (1983) Relationships among Ending Stocks, Prices, and Loan Rates for Corn, Feed Outlook and Situation Report. U.S. Department of Agriculture, Economic Research Service. 9-13. FdS-290.
[6]	Westcott, P.C. and Hoffman, L.A. (1999) Price Determination for Corn and Wheat: The Role of Market Factors and Government Programs. Market and Trade Economics Division, Economic Research Service. Technical Bulletin No. 1878.
[7]	World Bank (2015) The United States, World Development Indicators. http://data.worldbank.org/country/united-states
[8]	Lauer, J. (2002) Methods for Calculating Agricultural Yield. Field Crops (University of Wisconsin), 28, 47-33.
[9]	Furlong, F. and Ingenito, R. (1996) Commodity Prices and Inflation. Federal Reserve Bank of San Francisco Economic Review, 2, 27-47.
[10]	United States—Energy information Administration (US.EIA) (2015) Petroleum and Other Liquids. http://www.eia.gov/dnav/pet/pet_pri_spt_s1_d.htm
[11]	Kilian, L. and Murphy, D. (2014) The Role of Inventories and Speculative Trading in the Global Market for Crude Oil. Journal of Applied Econometrics, 29, 454-478. http://dx.doi.org/10.1002/jae.2322
[12]	Keatinge, J.D.H., Ledesma, D.R., Keatinge, F.J.D. and Hughes, J.D’A. (2014) Projecting Annual Air Temperature Changes to 2025 and Beyond: Implications for Vegetable Production Worldwide. The Journal of Agricultural Science, 152, 38-57. http://dx.doi.org/10.1017/S0021859612000913
[13]	Keatinge, J.D.H., Ledesma, D.R., Keatinge, F.J.D. and Hughes, J.d’A. (2015) Assessing the Value of Long Term Historical Air Temperature Records in the Estimation of Warming Trends for Use by Agricultural Scientists Globally. Advances in Agricultural Sciences, 3, 1-19.
[14]	Masters, M.W. (2011) Testimony of Michael W. Masters before the US Senate. In Lilliston, B. and Ranallo, A., Eds., Excessive Speculation in Agriculture Commodities: Selected Writings from 2008-2011, Institute for Agriculture and Trade Policy, Minneapolis, 89-94.
[15]	Spratt, S. (2013) Food Price Volatility and Financial Speculation’ Futures Agricultures. Working Paper, 1-21.
[16]	UNCTAD (2011) Price Formation in Financialized Commodity Markets: The Role of Information. Working Paper, United Nations Conference on Trade and Development.
[17]	Von Braun, J. and Torero, M. (2009) Implementing Physical and Virtual Food Reserves to Protect the Poor and Prevent Market Failure. IFPRI Policy Brief No. 10, International Food Policy Research Institute, Washington DC.
[18]	Karthikeyan, K. and Harlalka, A. (2014) Robust Regression Model for Prediction of Soybean Crop Price Based on Various Factors. International Journal of Emerging Technologies in Computational and Applied Sciences, 14, 258-263.
[19]	Roache, S.K. (2010) What Explains the Rise in Food Price Volatility? IMF Working Paper, No. 10/129.
[20]	Rangel, J.G. and Engle, R.F. (2008) The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes. The Review of Financial Studies, 21, 1187-1222. http://dx.doi.org/10.1093/rfs/hhn004
[21]	Roache, S.K. (2008) Commodities and the Market Price of Risk. IMF Working Paper, No. 08/221.