Please use this identifier to cite or link to this item: http://ir.lib.seu.ac.lk/handle/123456789/6996
Title: Analyzing cocoa export from Sri Lanka using a time series model
Authors: Wijerathne, R. G. W. I. M.
Jahufer, A.
Keywords: EGARCH
Cocoa Export
Forecast
Time Series Analysis
Issue Date: 14-Dec-2023
Publisher: Faculty of Applied Sciences, South Eastern University of Sri Lanka, Sammanthurai.
Citation: 12th Annual Science Research Sessions 2023 (ASRS-2023) Conference Proceedings of "Exploration Towards Green Tech Horizons”. 14th December 2023. Faculty of Applied Sciences, South Eastern University of Sri Lanka, Sammanthurai, Sri Lanka. pp. 52.
Abstract: The fundamental and indispensable component in the manufacturing of chocolate is known across the world as cocoa. Cocoa is a Pharmaceutical and chemical product, however, are additional significant cocoa-related products. These items have a significant global demand for cocoa, which has been identified as a high value export commodity in the export market. Sri Lanka has the necessary elements to cultivate cocoa. Then becoming a leader in the cocoa export market is a very simple task for us. This study examined the changes in Sri Lanka's cocoa export volume over 52-years from 1971 to 2021, and it provided the best statistical model for examining the changes. Time series analysis, a fundamental statistical concept, was employed in this study. EViews and MINITAB analytic software were used for the data analysis. Both ADF and KPSS unit root tests were used to check the stationarity of data set. Also used best model selection methods to select the best model among the fitted models. The EGARCH(0,2) model was identified as the most suitable and accurate statistical model for the examination of the amount of cocoa exported throughout the mentioned 52 years period. The use of this model will help export agriculture department officials as well as economic experts to develop and analyze the future of cocoa exports.
URI: http://ir.lib.seu.ac.lk/handle/123456789/6996
ISBN: 978-955-627-015-0
Appears in Collections:12th Annual Science Research Session

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