Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-16T18:45:20.163Z Has data issue: false hasContentIssue false

The Asymmetric Cycling of U.S. Soybeans and Brazilian Coffee Prices: An Opportunity for Improved Forecasting and Understanding of Price Behavior

Published online by Cambridge University Press:  26 January 2015

Octavio A. Ramirez*
Affiliation:
Department of Agricultural and Applied Economics, University of Georgia, Athens, GA
Get access

Abstract

The behavior of agricultural commodity markets can arguably result in markedly asymmetric price cycles, that is, downward cycles of substantially different length and breadth than upward cycles. This study assesses whether asymmetric-cycle models can enhance the understanding of the dynamics and provide for a better forecasting of U.S. soybeans and Brazilian coffee prices. The forecasts from asymmetric cycle models are found to be substantially mode precise than those obtained from standard autoregressive models. The asymmetric cycle models also provide useful insights on the markedly different dynamics of the upward vs. the downward cycles exhibited by the prices of these two commodities.

Type
Research Article
Copyright
Copyright © Southern Agricultural Economics Association 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Andrews, D.W.K.and Ploberger, W.Optimal Tests when a Nuisance Parameter is Present Only Under the Alternative.Econometrica 62(1994):13831414.Google Scholar
Balke, N.S., and Fomby, T.B.Threshold Cointegration.International Economic Review 38,3(1997):627–45.Google Scholar
Bradley, M., and Jansen, D.Non-Linear Business Cycle Dynamics: Cross-Country Evidence on the Persistence of Aggregate Shocks.Economic Inquiry 35(1997):495509.CrossRefGoogle Scholar
Brockwell, P.J., Liu, J., and Tweedie, R.On the Existence of Stationary Threshold Autoregressive Moving Average Processes.Journal of Time Series Analysis 13(1992):95107.Google Scholar
Chang, K.S.Consistency and the Limiting Distribution of the Least Squares Estimator of a Threshold Autoregressive Model.The Annals of Statistics 21(1993):520–33.Google Scholar
Davies, R.B.Hypothesis Testing when a Nuisance Parameter is Present Only Under the Alternative.Biometrika 74(1987):3343.Google Scholar
Enders, W., and Granger, C.W.J.Unit-Root Tests and Asymmetric Adjustment with an Example Using the Term Structure of Interest Rates.Journal of Business & Economic Statistics 16,3(1998):304–11.Google Scholar
Enders, W., and Siklos, PL.Cointegration and Threshold Adjustment.Journal of Business & Economic Statistics 19,2(2001):166–76.Google Scholar
Goodwin, B.K., and Piggott, N.E.Spatial Market Integration in the Presence of Threshold Effects.” American Journal of Agricultural Economics 83(2001):302–17.Google Scholar
Granger, C.W.J.and Lee, T.H.Investigation of Production, Sales and Inventory Relationships Using Multicointegration and Non-Symmetric Error-Correction Models.” Journal of Applied Econometrics 4(1989):S145–59.Google Scholar
Hansen, B.E.Inference in TAR Models.Studies in Nonlinear Dynamics and Econometrics, Berkeley Electronic Press 2,1(1997):114.Google Scholar
Hansen, B.E.Inference When a Nuisance Parameter is not Identified Under the Null Hypothesis.” Econometrica 64(1996):413–30.Google Scholar
Judge, G.G., Griffiths, W.E., Carter Hill, R., Lutkepohl, H., and Lee, T.-C.. The Theory and Practice of Econometrics. 2nd ed. New York: John Wiley & Sons, Inc., 1985.Google Scholar
Ker, A.P., and Coble, K.Modeling Conditional Yield Densities.American Journal of Agricultural Economics 85,2(2003):291304.Google Scholar
Moss, C.B., and Shonkwiler, J.S.Estimating Yield Distributions: Using a Stochastic Trend Model and Nonnormal Errors.” American Journal of Agricultural Economics 75,5 (1993):1056–62.Google Scholar
Nelson, CH., and Preckel, P.V.. 1989. “The Conditional Beta Distribution as a Stochastic Production Function.American Journal of Agricultural Economics 71,2(1989):370–78.Google Scholar
Obstfeld, M., and Taylor, A.M.Nonlinear Aspects of Goods-Market Arbitrage and Adjustment; Heckscher's Commodity Points Revisited.Journal of the Japanese and International Economies 11(1997):441–79.Google Scholar
Petrucelli, J.D., and Woolford, S.A Threshold AR(1) Model.Journal of Applied Probability 21(1984):270–86.Google Scholar
Potter, S.A Non-Linear Approach to U.S. GNP.Journal of Applied Econometrics 10(1995):109–25.Google Scholar
Ramirez, O.A.Estimation and Use of a Multivariate Parametric Model for Simulating Heteroskedastic, Correlated, Non-Normal Random Variables: The Case of Corn-Belt Corn, Soybeans and Wheat Yields.American Journal of Agricultural Economics 79,1(1997):191205.Google Scholar
Ramirez, O.A., and Somarriba, E.Risk and Returns of Diversified Cropping Systems under Non-Normal, Cross and Autocorrelated Commodity Price Structures.Journal of Agricultural and Resource Economics 25,2(2000):653–68.Google Scholar
Taylor, CR.Two Practical Procedures for Estimating Multivariate Non-Normal Probability Density Functions.American Journal of Agricultural Economics 72,1(1990):210–17.Google Scholar
Tong, H. Threshold Models in Non-Linear Time Series Analysis. Lecture Notes in Statistics 21. Berlin: Springer, 1983.Google Scholar
Tsay, R.S.Testing and Modeling Threshold Autoregressive Processes.Journal of the American Statistical Association 84(1989):231–40.Google Scholar