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Time Series Analysis of Price of Coffee in Case of Mettu Town, Ilu Ababor Zone, Oromia Regional State, Ethiopia

DOI: 10.18805/ajdfr.DR-204    | Article Id: DR-204 | Page : 279-284
Citation :- Time Series Analysis of Price of Coffee in Case of Mettu Town, Ilu Ababor Zone, Oromia Regional State, Ethiopia.Asian Journal of Dairy and Food Research.2021.(40):279-284
Dereje Gebeyehu Ababu, Azmeraw Misganaw Getahun derejegebe@gmail.com
Address : Department of Statistics, Faculty of Natural and Computational Science, Mettu University, Ethiopia.
Submitted Date : 15-10-2020
Accepted Date : 31-03-2021

Abstract

Background: Coffee is one of the most important cash crops across the world and major source of export earnings. Coffee has been and remains the leading cash crop and export commodity of Ethiopia. The aim of this study was to estimate and predict the price change of coffee in Mettu town.
Methods: In this study both descriptive and inferential statistics were used to analyze secondary data that were collected from Mettu town of Ethiopian Commodity Exchange office sector. A total of 120 months of price of coffee was included in this study. Time series analysis was used to estimate the parameter and for forecast the future values of price change of coffee.
Result: The original data was not stationary and become stationary after second order differencing. The results showed that the price change of coffee was increasing from time to time. After that the data tested the order MA and AR are identified by using the ACF and PACF. Then the model was selected by using AIC. Since, ARMA (1, 2, 1) for price change of coffee was lower values of AIC found to be the most appropriate model to fit the data of the price change of coffee. After the model was fitted, the diagnostic checking has been applied by using the ACF residual and normality checking. So that the model fitted is appropriate for the price change of coffee. All the forecast values are found between the lower and the upper interval then we can say that forecasted value is accurate.

Keywords

ARIMA ACF Coffee Price PACF Time series

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