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

Abstract

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.