Acta Univ. Agric. Silvic. Mendelianae Brun. 2016, 64(4), 1285-1293 | DOI: 10.11118/actaun201664041285

Short-Term and Long-Term Relationships Between Prices of Imported Oil and Fuel Products in the U. S.

Václav Adamec
Department of Statistics and Operations Research, School of Business and Economics, Mendel University in Brno, Zemìdìlská 1, 613 00 Brno, Czech Republic

In this study, we analyzed a system of five monthly time series integrated I(1): average price of crude oil imported to the U.S. from OPEC countries (Opec), imported oil price from other than OPEC countries (NonOpec) in USD per barrel, average price of regular gasoline in the U.S. (Regular), premium quality gasoline price (Premium) and kerosene price (Kerosene) in U.S. cents per gallon. Cointegration was established by EG test and the series were analyzed by VECM model with lag selected via BIC criterion. Cointegration rank was determined by the Johansen procedure. According to VECM coefficients, prices of oil from OPEC countries and beyond OPEC exert influence upon all commodity prices in the system, but in a contradictory manner. Responses to innovation shocks in Opec and NonOpec stabilized within 8 to 10 months upon a nonzero shift and further became permanent. Innovation shock in both types of gasoline and Kerosene had only short-term significant impact upon the system. Forecast error variance in all variables is explained mainly by variation in oil prices, especially Opec, which persists with increased horizon. For a short horizon h = 1, FEVDs in gasoline and kerosene prices are primarily made of variation in the respective fuel prices.

Keywords: oil price, cointegration, vector error correction model, impulse-response function, forecast error variance decomposition, R-software

Prepublished online: August 30, 2016; Published: September 1, 2016  Show citation

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Adamec, V. (2016). Short-Term and Long-Term Relationships Between Prices of Imported Oil and Fuel Products in the U. S. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 64(4), 1285-1293. doi: 10.11118/actaun201664041285
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    References

    1. AL-GUDHEA, S., KENC, T., and DIBOOGLU, S. 2007. Do retail gasoline prices rise more readily than they fall? A threshold cointegration approach. Journal of Economics and Business, 59: 560 - 574. DOI: 10.1016/j.jeconbus.2006.10.002 Go to original source...
    2. BORENSTEIN, S., CAMERON, A. C., and GILBERT, R. 1997. Do gasoline prices respond asymmetrically to crude oil price changes? The Quarterly Journal of Economics, 112: 305 - 339. DOI: 10.1162/003355397555118 Go to original source...
    3. BROCKWELL, P. J., and DAVIS, R. A. 1991. Time series: theory and methods. 2nd Edition. Berlin: Springer Verlag. Go to original source...
    4. BUMPASS, D., GINN, V., and TUTTLE, M. H. 2015. Retail and wholesale gasoline price adjustments in response to oil price changes. Energy Economics, 52(A): 49-54. DOI: 10.1016/j.eneco.2015.08.030 Go to original source...
    5. CHEN, L-H., FINNEY, M., and LAI, K. S. 2005. A threshold cointegration analysis of asymmetric price transmission from crude oil to gasoline prices. Economics Letters, 89: 233 - 239. DOI: 10.1016/j.econlet.2005.05.037 Go to original source...
    6. DICKEY, D. A., and FULLER, W. A. 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49: 1057 - 1071. DOI: 10.2307/1912517 Go to original source...
    7. ENERGY INFORMATION ADMINISTRATION. 1999. Price changes in the gasoline market. [Online]. DOE/EIA-0626. Available at http://www.eia.gov/pub/oil_gas/.
    8. ENGLE, R. F., and GRANGER, C. W. J. 1987. Co-integration and error correction: representation, estimation and testing. Econometrica, 55: 251 - 276. DOI: 10.2307/1913236 Go to original source...
    9. GHASSAN, H. B., and BANERJEE, P. K. 2015. A threshold cointegration analysis of asymmetric adjustment of OPEC and non-OPEC monthly crude oil prices. MPRA Paper No. 62168. Available at https://mpra.ub.uni-muenchen.de/62168/.
    10. HUİEK, R. 2007. Ekonometrická analŭza. 1st Edition. Praha: Oeconomica.
    11. JOHANSEN, S. 1991. Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models. Econometrica, 59(6): 1551 - 1580. DOI: 10.2307/2938278 Go to original source...
    12. KWIATKOWSKI, D., PHILLIPS, P. C. B., SCHMIDT, P., and SHIN, Y. 1992. Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54: 159 - 178. DOI: 10.1016/0304-4076(92)90104-Y Go to original source...
    13. LÛTKEPOHL, H. 2006. New introduction to multiple time series analysis. 1st Edition. Berlin: Springer Verlag. Go to original source...
    14. MacKINNON, J. G. 1996. Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11: 601 - 618. DOI: 10.1002/(SICI)1099-1255(199611)11:63.0.CO;2-T Go to original source...
    15. PFAFF, B. 2008a. Analysis of integrated and cointegrated time series with R. 2nd Edition. New York: Springer Verlag. Go to original source...
    16. PFAFF, B. 2008b. VAR, SVAR and SVEC models: implementation within R package vars. Journal of Statistical Software, 27(4): 1 - 32. DOI: 10.18637/jss.v027.i04 Go to original source...
    17. PFAFF, B. 2013a. urca: Unit root and cointegration tests for time series data. R package version 1.2- 8.
    18. PFAFF, B. 2013b. vars: VAR modelling. R package version 1.5-2.
    19. PHILLIPS, P. C. B., and OULIARIS, S. 1990. Asymptotic properties of residual based tests for cointegration. Econometrica, 58: 165 - 193. DOI: 10.2307/2938339 Go to original source...
    20. R DEVELOPMENT CORE TEAM. 2015. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: www.r-project.org.
    21. TSAY, R. S. 2005. Analysis of financial time series. 2nd Edition. London: Wiley. Go to original source...
    22. ZIVOT, E., and WANG, J. 2006. Modeling financial time series with S-Plus. 2nd Edition. Berlin: Springer Verlag.

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