<div>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&nbsp;was&nbsp;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.</div>

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

India is witnessing tremendous growth in dairy industry. The milk production has increased from 20 million tonnes in 1961 to 132 million tonnes in 2012-13. India has been retaining its number one position in milk production for many years. Dairy Industry in India is growing at the rate of 10% per annum. Considering this, it is essential to know the future production to improve and sustain the growth and development of sector. The objective of the study is to find out most suitable forecasting method for milk production for sustainable future production and policy implications. The data used in study is secondary data, collected from FAOSTAT (1961 to 2012) and NDDB (1991 to 2012). Stationarity of data was checked with Autocorrelation Function (ACF) and Partial autocorrelation function (PACF), after confirming the stationarity, Autoregressive Integrated Moving Average (ARIMA) and Vector Autoregression (VAR) models were used. Akaike Information Criteria (AIC), Schwartz Bayesian Criteria (SBC), Mean Absolute Percentage Error (MAPE), R square and RMSE were used to test reliability of model. The results indicate that ARIMA (1, 1, 1) is more suitable method with the use of SPSS software package for forecasting of milk. Milk production is expected to be 160 million tonnes by 2017.&nbsp;

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

Forecasting of milk production in India with ARIMA and VAR time series models