Modelling and forecasting of pigeonpea (Cajanus cajan) production using autoregressive integrated moving average methodology

Abstract

A study was conducted on modelling and forecasting time-series data of pigeonpea production [Cajanus cajan (L.) Millsp.] in India. Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) time-series methodology was considered for modelling and forecasting country's pigeonpea production data (1969–70 to 2007–08). The augmented Dicky Fuller test was applied to test stationarity in data set. Root mean square error, Akaike information criterion and Bayesian information criterion were used to identify the best model. The performance of fitted model was examined using mean absolute error, mean per cent forecast error, root mean square error and Theil's inequality coefficients. ARIMA (2, 1, 0) model performed better among other models of ARIMA family for modelling as well as forecasting purpose. One and two-step ahead forecast value for 2006–07 and 2007–08 for India's pigeonpea production was computed as 2.54 and 2.53 million tonnes with standard errors 0.29 and 0.31, respectively.