Abstract
This study examines the patterns in the export of wood products in Ghana from 1997-2013. We also build a time series model to forecast the volume of wood products export over the same period. The study employs the Box-Jenkins methodology of building ARIMA (Autoregressive Integrated Moving Average) model. Monthly time series data on exports of wood products from 1997-2013 were extracted from monthly and annual reports on export of wood products published by the Timber Industry Development Division (TIDD) of the Forestry Commission of Ghana. Different selected models were tested to ensure the accuracy of obtained results and ARIMA (3, 1, 0) (0, 1, 1)_{12} was adjudged the best model. This model was then used to forecast the volume of wood products export for 2014 and 2015. January and June represent the minimum and maximum export periods respectively. The model will guide TIDD in their annual timber export planning and also help avoid financial losses that could result from poor decision making and ultimately improve efficiency of their operations.
Highlights
The forestry sector plays an important role in the Ghanaian economy
Two hundred and four (204) monthly data on the volume (m3) and value (Euros) of wood products export from January 1997 to December 2013 were obtained from the monthly and annual reports on export of wood products by the Timber Industry Development Division (TIDD) of the Forestry Commission of Ghana
Based on the inspection of the decomposition plot, we considered fitting a seasonal Autoregressive Integrated Moving Average (ARIMA) model to the data
Summary
The forestry sector plays an important role in the Ghanaian economy. Its contribution to Gross Domestic Product (GDP) increased from 2.5% in 1991 to 8% in 1997 (Oduro, Duah-Gyamfi, Acquah, & Agyeman, 2012). Timber export earned Ghana around 10% of the foreign exchange between 1990 and 2000 but there has been a considerable decline since 2005 from 8.1% to about 1.3% in 2011 (FIP, 2012). This decline has been attributed to ongoing dwindling natural tropical forest resource base, low production recovery rates, wood wastes and illegal chainsaw activities (Oduro, Mohren, Affum-Baffoe, & Kyereh, 2014; Hansen, Damnyag, Obiri, & Carlsen, 2012; Marfo, 2010; Food and Agriculture Organization (FAO), 2005; Forestry Outlook Study for Africa (FOSA), 2001). Companies that had managed to survive the turbulence in the industry are currently producing below 50 per cent capacity (Daily Guide, 2012)
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