The Coconut Industry: A Review of Price Forecasting Modelling in Major Coconut Producing Countries
Keywords:
Coconut, modelling, price forecasting, time series models, system's approach
Abstract
The global supply and demand of coconuts and coconut-based products have been increased tremendously over the past decades; hence, the industry has become one of the significant contributors to the economies of producer countries. However, similar to the other agricultural industries, coconut has confronted by fluctuation in prices and accords the importance of reliable price modelling and forecasting techniques to ease the burden on the value chain actors. Therefore, the objective of this paper is to review the main approaches used in modelling and forecasting coconut prices, with an assessment of the strengths and weaknesses of each approach. The modelling techniques used in coconut price forecasting were mainly time series models dominated by univariate time series models. This type of models excessively confines the analysis to a single variable, despite the many interactions affected in a system of coconut pricing. The major drawback in existing price modelling studies is the absence of interacting factors such as prices, production, climatic variables and their interactions as a system. Therefore, it is important to extend the existing studies of coconut price modelling and forecasting with a system’s approach by including other influencing variables to generate more realistic forecast values, allowing the industry to adopt its changing circumstances.
Keywords: Coconut, modelling, price forecasting, time series models, system’s approach
References
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Peiris, T. S. G., Thattil, R. O. & Mahindapala, R., 1995. An Analysis of the Effect of Climate and Weather on Coconut (Cocos nucifera). Experimental Agriculture, Volume 31, pp. 451-460.
Peterson, H. H. & Tomek, W. G., 2000. Implications of Deflating Commodity Prices for Time-Series Analysis. Chicago, s.n.
Priyadarshani, G. A. C. N., Thilakaratne, C. D., Sunethra, A. A. & Oshani, D. K., 2014. Modeling Monthly Coconut Prices in Sri Lanka using Non-Linear Time Series Models. Sri Lanka, s.n., p. 229.
Rangoda, B. D. P., Abeywickrama, L. M. & Fernando, M. T. N., 2006. An analysis of different forecasting models for prices of coconut products in Sri Lanka. s.l., University of Ruhuna, pp. 8-15.
Rethinam, P., 2005. Asian and Pacific Coconut Community Activities, Achievements and Future Outlook. Cairns, Australia, Australian Centre for International Agricultural Research, pp. 15-21.
Sri Lanka Export Development Board, 2017. Growth of Demand for Coconut in the Global Market. [Online]
Available at: https://www.srilankabusiness.com/blog/growth-of-global-demand-for-coconut.html
[Accessed 4 11 2018].
Taylor, H. C., 1924. Agricultural Forecasting. American Journal of Agricultural Economics, 6(2), pp. 156-163.
Adebiyi, A. A., Adewumi, A. O. & Ayo, C. K., 2014. Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction. Journal of Applied Mathematics, pp. 1-7.
Alibuhtto, M. C., 2013. Modelling Fresh Coconuts Export using Time Series Approach. Oluvil, Sri Lanka, South Eastern University of Sri Lanka, pp. 49-53.
Allen, P. G., 1994. Economic Forecasting in Agriculture. International Journal of Forecasting, Volume 10, pp. 81-135.
Ang, L. C. Y., 2016. A forecast of the monthly price of copra using TARMO, s.l.: s.n.
Aponsu, G. M. L. M. & Jayasundara, D. D. M., 2012. Time Fluctuation Models to Forecast Tea Production, Prices and Exports in Sri Lanka.. Colombo, University of Kelaniya.
Asian Pacific Coconut Community, 2016. Coconut Statistical Yearbook, Jakarta: Asian Pacific Coconut Community (APCC).
Brintha, N. K. K. et al., 2014. Use of Unobserved Components Model for Forecasting Non-stationary Time Series: A Case of Annual National Coconut Production in Sri Lanka. Tropical Agricultural Research, 25(4), pp. 523-531.
Cherdchoongam, S. & Rungreunganum, V., 2016. Forecasting Prices of Natural Rubber in Thailand Using ARIMA Model. KMUTNB International Journal of Applied Science Technology, 9(4), pp. 271-277.
Das, P. K., 1986. Movement of Wholesale Prices of Coconuts, Copra and Coconut Oil in Kerala during the last Two and Half Decades. Journal of Plantation Crops, 14(2), pp. 105-114.
Deaton, A. & Laroque, G., 1992. On the Behaviour of Commodity Prices. The Review of Economic Studies, 59(1), pp. 1-23.
Elias, G., 2018. Economics of coconut products- an analytical study. Commerce Spectrum, 5(2), pp. 39-44.
Estal, B. R., 2014. Pricing Movements of Copra in the Philippines. In: Handbook on the Emerging Trends in Scientific Research. Malaysia: PAK Publishing Group, pp. 527-534.
FAO, 2017. FAOSTAT. [Online]
Available at: http://www.fao.org/faostat/en/#data
[Accessed 12 11 2018].
Gujarati, d. N. & Porter, D. C., 2008. Basic Econometrics. 5 ed. New York: McGraw-Hill/Irwin.
Gunathilaka, R. P. D. & Tularam, G. A., 2016. The Tea Industry and a Reveiw of Its Price Modelling in Major Tea Producing Countries. Journal of Management and Strategy, 7(1), pp. 21-33.
Harris, E., Aziz, A. R. A. & Avuglah, R. K., 2012. Modeling Annual Coffee Production in Ghana Using ARIMA Time Series Model. International Journal of Business and Social Research, 2(7), pp. 175-186.
Harvey, A. C., 1993. Time Series Models. 2 ed. Cambridge: The MIT Press.
Hettiarachchi, H. A. C. K. & Banneheka, B. M. S. G., 2013. Time Series Regression and Artificial Neural Network Approaches for Forecasting Unit Price of Tea at Colombo Auction. Journal of National Science Foundation Sri Lanka, 41(1), pp. 35-40.
Hussain, M. N. & Ali, A., 2017. Forecasting of Pakistan’s Import Prices of Black Tea Using ANN and SARIMA Model. International Review of Management and Business Research, 6(4), pp. 1372-1382.
Ikonya, M., Mwita, P. & Wanjoya, A., 2014. Modeling export price of tea in Kenya: Comparison of artificial neural network and seasonal autoregressive integrated moving average. American Journal of Theoretical and Applied Statistics, 3(6), pp. 211-216.
Indraji, K. N., 2014. Price forecast models for coconut and coconut oil, s.l.: s.n.
Induruwage, D., Tilakaratne, C. D. & Rajapaksha, S. R. M. S. P., 2016. Forecasting Black Tea Auction Prices by Capturing Common Seasonal Patterns. Sri Lankan Journal of Applied Statistics, 16(3), pp. 195-214.
Jayalath, K. V. N. N. & Weerahewa, J., 2014. Tariff Endogeneity: Effect of Export Price of Desiccated Coconuts on Edible Oil Market in Sri Lanka. Tropical Agricultural Research, 25(4), pp. 476-486.
Jha, G. K. & Sinha, K., 2013. Agricultural Price Forecasting Using Neural Network Model: An Innovative Information Delivery System. Agricultural Economics Research Review, 26 (2), pp. 229-239.
Just, R. E., 1993. Discoveringproduction and Supply Relationships: Present Status and Future Opportunities. Review of Marketing and Agricultural Economics, Volume 61, pp. 11-40.
Kalidas, K., Darthiya, M., Malathi, P. & Thomas, L., 2014. Organic Coconut Cultivation in India –Problems & Prospects. International Journal of Scientific Research, 3(6), pp. 14-15.
Li, G.-q., Xu, S.-w. & Li, Z.-m., 2010. Short-Term Price Forecasting for Agro-Products using Artificial Neural Networks. s.l., s.n., pp. 278-287.
Liu, H. & Shao, S., 2016. India’s Tea Price Analysis Based on ARMA Model. Modern Economy, Volume 7, pp. 118-123..
Makridakis, S., Hibon , M. & Moser, C., 1979. Accuracy of Forecasting: An Empirical Investigation. Journal of the Royal Statistical Society .Series A (General), 142(2), pp. 97-145.
Meade, N., 2000. Evidence for the Selection of Forecasting Methods. Journal of Forecasting, Volume 19, pp. 515-535.
Nampoothiri, C. K. & Balakrishna, N., 2000. Threshold Autoregressive Model for a Time Series Data. Journal of Indian Soc. Agricultural Statistics, 53(2), pp. 151-160.
Nampoothiri, K., 2001. Modelling and Analysis of some Time Series, s.l.: s.n.
Naveena, K., Rathod, S., Shukla, G. & Yogish, K. J., 2014. Forecasting of coconut production in India: A suitable time series model. International Journal of Agricultural Engineering, 7(1), pp. 190-193.
Nochai, R. & Titida Nochai, 2006. ARIMA model for Forecasting Oil Palm Price. Penang, University Sains Malaysia, pp. 1-7.
Nyantakyi, K. A., Peiris, B. L. & Gunaratna, L. H. P., 2015. Analysis of the Interrelationships between the Prices of Sri Lankan Rubber, Tea and Coconut Production Using Multivariate Time Series. Advances in Economics and Business, 3(2), pp. 50-56.
Pathiraja, P. M. E. K., Griffith, G. R., Farquharson, R. J. & Faggin, R., 2015. The Sri Lankan Coconut Industry: Current Status and Future Prospects in a Changing Climate. Australasian Agribusiness Perspectives, Paper 106, pp. 1-23.
Peiris, T. S. G., Thattil, R. O. & Mahindapala, R., 1995. An Analysis of the Effect of Climate and Weather on Coconut (Cocos nucifera). Experimental Agriculture, Volume 31, pp. 451-460.
Peterson, H. H. & Tomek, W. G., 2000. Implications of Deflating Commodity Prices for Time-Series Analysis. Chicago, s.n.
Priyadarshani, G. A. C. N., Thilakaratne, C. D., Sunethra, A. A. & Oshani, D. K., 2014. Modeling Monthly Coconut Prices in Sri Lanka using Non-Linear Time Series Models. Sri Lanka, s.n., p. 229.
Rangoda, B. D. P., Abeywickrama, L. M. & Fernando, M. T. N., 2006. An analysis of different forecasting models for prices of coconut products in Sri Lanka. s.l., University of Ruhuna, pp. 8-15.
Rethinam, P., 2005. Asian and Pacific Coconut Community Activities, Achievements and Future Outlook. Cairns, Australia, Australian Centre for International Agricultural Research, pp. 15-21.
Sri Lanka Export Development Board, 2017. Growth of Demand for Coconut in the Global Market. [Online]
Available at: https://www.srilankabusiness.com/blog/growth-of-global-demand-for-coconut.html
[Accessed 4 11 2018].
Taylor, H. C., 1924. Agricultural Forecasting. American Journal of Agricultural Economics, 6(2), pp. 156-163.
Published
2020-12-22
How to Cite
Abeysekara, M. G. D., & Waidyarathne, K. (2020). The Coconut Industry: A Review of Price Forecasting Modelling in Major Coconut Producing Countries. CORD, 36, 17-26. https://doi.org/10.37833/cord.v36i.422
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