Statistical Modeling to Forecast the Wood-Based Panels Consumption in Iran

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Research Paper 15/06/2014
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Statistical Modeling to Forecast the Wood-Based Panels Consumption in Iran

Ajang Tajdini, Amir Tavakkoli, Ahmad Jahan Latibari, Mehran Roohnia, Shademan Pourmousa
Int. J. Biosci.4( 12), 1-11, June 2014.
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Abstract

In this paper, the consumption of wood -based panels in Iran are forecasted up to the year 2014 using statistical time series exponential smoothing and ARIMA models. The models performance was calculated in term of RMSE. ADF test was applied to investigate the stationary character of the data. The results indicated that the Holt-winters exponential smoothing model with the smallest RMSE can be selected as the best forecasting model for particleboard and plywood. The ARIMA (2,1,1) model provided the smallest RMSE and it was selected as the best forecasting model for veneer. Forecasting accuracy of the Holt-Winters model is more than the double exponential smoothing model, especially in the case of plywood. It was projected that consumption levels particleboard, veneer and plywood to increase and then decrease from 2010 to 2014 respectively. The most significant increase is forecasted in the consumption of veneer and particleboard. The average annual rates of increase are calculated as 5.1% and 1.17% for veneer and particleboard respectively. For plywood, the average annual rate of decrease is 3%. Particleboard. The consumption quantity of particleboard and veneer will increase from 684790 and 115880638 m2 in 2009 to 749428 and 206424496 in 2014 respectively. For plywood, the consumption quantity will be reduced from 32000 in 2009 to 23035 m3 in 2014.

VIEWS 1

Alexander K. 2003. An industrial application of time series forecasting of lumber demand. M.Sc thesis, North Carolina State University.

Azizi M, Ghorbanzadeh P, Hatefnia H. 2008. Estimation of demand for wood panels in Iran by the year of 2012. Journal of Forestry Research 20, 179-182. http://dx.doi.org/10.1007/s11676-009-0033-z

Clements M, Hendry D. 1998. Forecasting economic time series. Cambridge University Press, Cambridge.

FAO. 1999. Global forest products consumption, production, trade and prices: global forest products model projections to 2010. Working paper series: No: GFPOS/WP/01. http://ftp://ftp.fao.org/docrep/fao/003/X1607E/X1607E00.pdf. Accessed 4 April 2012.

FAO. 2009. State of the World’s Forests (Global demand for wood products). ftp://ftp.fao.org/docrep/fao/011/i0350e/i0350e02a.pdf. Accessed 15 April 2012.

Gujarati DN. 2004. Basic Econometrics. 4th ed. The McGraw-Hill, New York.

Hamilton JD. 1994. Time Series Analysis. Princeton University Press, Princeton, New Jersey.

Hanninen R. 2004. Econometric models in forest sector forecasting. Journal of Forest Economics. 10, 57-59. http://dx.doi.org/10.1016/j.jfe.2004.07.002

Harvey AC. 1989. Forecasting, structural time series analysis. Princeton University Press. Princeton, New Jersey.

Hetemaki L, Obersteiner M. 2001. US Newsprint Demand Forecasts to 2020. University of California. Accessed 27 February 2011. http://Berkeley.groups.haas.berkeley.edu/fcsuit/PDF-papers/LauriFisherPaper.pdf.

Hetemaki L, Mikkola J. 2005. Forecasting Germany’s printing and writing paper imports. Forest Science 51(5), 483-497.

Malaty R, Toppinen A, Vitanen J. 2007. Modeling and forecasting Finnish Pine sawlog stumpage prices using alternative time-series methods. Canadian Journal of Forest Research. 37, 178-187. http://dx.doi.org/10.1139/x06-208

Mofrad H. 2010. Investigation of effective factors on demand for import functions of the most important Wood-based Panels in Iran and import projection. M. Sc. Thesis, Islamic Azad University –Karaj branch (In Persian).

Mohammadi Limaei S, Heybatian R, Heshmatol Vaezin S M, Torkman J. 2011. Wood import and export and its relation to major macroeconomics variables in Iran. Forest Policy and Economics 13, 303-307. http://dx.doi.org/10.1016/j.forpol.2011.03.001

Pesaran MH, Pesaran B. 1997. Microfit 4.0 (Windows  Version) Oxford University  Press,  New York.

Song N. 2006. Structural and forecasting Softwood lumber models with a time series approach. Dissertation, Louisiana State University and Agricultural and Mechanical College.

Tajdini A, Tavakkoli A, Latibari AJ, Roohnia M. 2011. Application of simultaneous equations model to estimate particleboard demand and supply. BioResources. 6(3), 3199-3209.