Relation of pine chip-n-saw to sawtimber and pulpwood prices in the Southeastern United States: direction of influence
Main Article Content
Keywords
chip-n-saw, cointegration, pine pulpwood, pine sawtimber, The Law of One Price, Timber Mart-South
Abstract
Relationships among prices of pine sawtimber (PST), pine pulpwood (PP), and chip-n-saw (CNS) were examined for southeastern markets in the United States. The data were extracted from the Timber Mart-South database and included quarterly prices of pine products from 1979 to 2016 for markets in Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, and Texas. The data were separated into two regions in each State. Both regions were used for Alabama, Florida, Georgia, Mississippi, and South Carolina, but only single regions were used for Arkansas, Louisiana, North Carolina, and Texas. The number of significant lags indicated by the Akaike information criterion varied between one and three for all markets, and those lags were used for further analysis. The Granger causality test using the Yamamoto–Toda method indicated significant predictability of PST by CNS in four regions, PST by PP in three regions, CNS by PST in three regions, CNS by PP by three regions, and PP by PST by two regions. The Granger causality test using a differencing method indicated significant predictability for two fewer regions than the Yamamoto-Toda method, with eight regions in common. Of all the regions, the highest number of significant causalities was in region 1 of Alabama and region 2 of Georgia; no causalities were significant in regions 1 of Arkansas and Louisiana. Based on the number of significant predictabilities, the strongest causality was for prediction of CNS by PST, and the weakest was for prediction of PP by CNS. The results help better understand price relationships among timber stumpage products, the degree of substitutability among them, and the importance of individual market characteristics.
References
Bauer D, Maynard A. 2012. Persistence-robust surplus-lag Granger causality testing. Journal of Econometrics 169(2):293-300. https://doi.org/10.1016/j.jeconom.2012.01.023
Bingham MF, Prestemon JP, MacNair DJ, Abt RC. 2003. Market structure in US southern pine roundwood. Journal of Forest Economics 9(2):97-117. https://doi.org/10.1078/1104-6899-00025
Cheung YW, Lai KS. 1995. Lag order and critical values of the augmented Dickey-Fuller test. Journal of Business & Economic Statistics 13(3):277-280. https://doi.org/10.2307/1392187
Clarke JA, Mirza S. 2006. A comparison of some common methods for detecting Granger noncausality. Journal of Statistical Computation and Simulation 76(3):207-231. https://doi.org/10.1080/10629360500107741
Comincioli B. 1996. The stock market as a leading indicator: An application of Granger causality. University Avenue Undergraduate Journal of Economics, 1(1),1. http://digitalcommons.iwu.edu/uauje/vol1/iss1/1
Davidson R, MacKinnon JG. 1993. Estimation and Inference in Econometrics. New York, New York: Oxford University Press.
Dickey DA, Fuller WA. 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74(366):427-431. https://doi.org/10.2307/2286348
Dolado JJ, Lütkepohl H. 1996. Making Wald tests work for cointegrated VAR systems. Economic Reviews 15(4):369-386. https://doi.org/10.1080/07474939608800362
Fugarolas G, Mañalich I, Matesanz D. 2007. Are exports causing growth? Evidence on international trade expansion in Cuba, 1960-2004. Retrieved from: https://mpra.ub.uni-muenchen.de/6323
Giles JA, Mirza S. 1999. Some pretesting issues on testing for Granger noncausality. Econometrics Working Paper EWP9914, Department of Economics, University of Victoria.
Giles JA, Williams CL. 2000. Export-led growth: A survey of the empirical literature and some non-causality results. Part 1. The Journal of International Trade & Economic Development 9(3):261-337.
https://doi.org/10.1080/09638190050086177
Granger CW. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3):424-438. https://doi.org/10.2307/1912791
Hamilton JD. 1994. Time Series Analysis. Princeton, N.J.: Princeton University Press.
Hood HB, Dorfman JH. 2015. Examining dynamically changing timber market linkages. American Journal of Agricultural Economics 97(5):1451-1463. https://doi.org/10.1093/ajae/aau151
Howard J, Jones KC. 2019. U.S. Timber production, trade, consumption, and price statistics, 1965-2017 (Research Paper FPL-RP-679). Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory.
https://doi.org/10.2737/FPL-RP-701
Ivanov V, Kilian L. 2001. A practitioner's guide to lag-order selection for vector autoregressions. CEPR Discussion Paper 2685, Centre for Economic Policy Research.
Johansen, S. 1995. Likelihood-based inference in cointegrated vector autoregressive models. Oxford, UK: Oxford University Press. https://doi.org/10.1093/0198774508.001.0001
Johansen S. 2006. Cointegration: An overview. In: Palgrave Handbook of Econometrics, Volume I. Basingstoke, UK: Palgrave Macmillan: p. 540-577.
Judge GG, Griffiths WE, Hill RC, Lütkepohl H, Lee TC. 1985. The theory and practice of econometrics, 2nd edition. New York, NY: John Wiley and Sons.
Lütkepohl H. 2005. New introduction to multiple time series analysis. Berlin Heidelberg: Springer-Verlag.
https://doi.org/10.1007/978-3-540-27752-1
Mei B, Clutter M, Harris T. 2010. Modeling and forecasting pine sawtimber stumpage prices in the US South by various time series models. Canadian Journal of Forest Research 40(8): 1506-1516. https://doi.org/10.1139/X10-087
Misztal MD, Siry J, Mei B, Harris Y, Bowker JM. 2024. Accounting for exogenous shocks in determining Southeastern U.S. timber markets. Mathematical and Computational Forestry and Natural Resources Sciences 16(1):14-26.
Nagubadi V, Munn IA, Ahai AT. 2001. Integration of hardwood stumpage markets in the Southcentral United States. Journal of Forest Economics 7(1):69-98.
Ning Z, Sun C. 2014. Vertical price transmission in timber and lumber markets. Journal of Forest Economics 20(1):17-32. https://doi.org/10.1016/j.jfe.2013.07.002
Parajuli R, Tanger S, Abt R, Cubbage F. 2019. Subregional timber supply projections with chip-n-saw stumpage: Implications for southern stumpage markets. Forest Science 65(6):665-669. https://doi.org/10.1093/forsci/fxz044
Parajuli R, Tanger SM, Joshi O, Henderson JE. 2016. Modeling prices for sawtimber stumpage in the South-Central United States. Forests 7(7):148. https://doi.org/10.3390/f7070148
Park JY, Phillips PCB. 1989. Statistical inference in regressions with integrated processes: Part 2. Econometric Theory 5(1):95-131. https://doi.org/10.1017/S0266466600012287
Prestemon JP. 2003. Evaluation of U.S. southern pine stumpage market informational efficiency. Canadian Journal of Forest Research 33(4):561-572. https://doi.org/10.1139/x02-198
Prestemon JP, Pye JM. 2000. A technique for merging areas in Timber Mart-South data. Southern Journal of Applied Forestry 24(4):219-229. https://doi.org/10.1093/sjaf/24.4.219
Prestemon JP, Wear DN. 1999. Inventory effects on aggregate timber supply. In SOFEW '98: Proceedings of the 1998 Southern Forest Economics Workshop: p. 26-32.
Schwarz G. 1978. Estimating the dimension of a model. Annals of Statistics 6(2):461-464. https://doi.org/10.1214/aos/1176344136
Sims CA, Stock JS, Watson MW. 1990. Inference in linear time series models with some unit roots. Econometrica 58(1):113-144. https://doi.org/10.2307/2938337
Tanger S, Parajuli R. 2018. Toward an elasticity of chip-n-saw: Demand and supply models of chip-n-saw stumpage in Louisiana. Forests 9:211. https://doi.org/10.3390/f9040211
Thornton DL, Batten DS. 1985. Lag-length selection and tests of Granger causality between money and income. Journal of Money, Credit and Banking 17(2):164-178. https://doi.org/10.2307/1992331
Toda HY, Yamamoto T. 1995. Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics 66(1-2):225-250. https://doi.org/10.1016/0304-4076(94)01616-8
Uri ND, Boyd R. 1990. Considerations on modeling the market for softwood lumber in the United States. Forest Science 36(3):680-692. https://doi.org/10.1093/forestscience/36.3.680
Yin R, Newman DH, Siry J. 2002. Testing for market integration among southern pine regions. Journal of Forest Economics 8(2):151-166. https://doi.org/10.1078/1104-6899-00009
Zhou M, Buongiorno J. 2005. Price transmission between products at different stages of manufacturing in forest industries. Journal of Forest Economics 11(1):5-19. https://doi.org/10.1016/j.jfe.2005.02.002