Time Series Modeling for Trend Analysis and Forecasting Wheat Production of India

Wheat is one of the most important staple food grains of human for centuries. It has a special place in the Indian economy because of its significance in food security, trade and industry. This study made an attempt to model and forecast the production of wheat in India by using annual time series data from 1961–2013. Parametric regression, exponential smoothing and Auto Regressive Integrated Moving Average (ARIMA) models were employed and compared for finding out an appropriate econometric model to capture the trend of wheat production of the country. The best fitted model was selected based on the performance of several goodness of fit criteria viz. Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Akaike Information Criterion (AIC), Schwarz's Bayesian Information Criterion (SBC) and R-squared values. The assumptions of ‘Independence’ and ‘Normality’ of error terms were examined by using the ‘Runtest’ and ‘Shapiro-Wilk test’ respectively. This study found ARIMA (1,1,0) as most appropriate to model the wheat production of India. The forecasted value by using this model was obtained as 100.271 million tones (MT) by 2017–18.