Modelling and forecasting of retail price of arhar dal in Karnal, Haryana

Abstract

Forecasting retail price of agricultural commodities is of utmost importance for planning in advance to resist any abnormalities. In this regard, the autoregressive fractionally integrated moving-average (ARFIMA) model is employed. ARFIMA model searches for a non-integer differencing parameter d to difference the data to capture long memory. The model is applied for modelling and forecasting of daily retail price of pigeonpea (Cajanas cajan) in Karnal during January, 2011 to July, 2013. Augmented Dickey-Fuller (ADF) test and Philips Peron (PP) test are used for testing the stationarity of the series. Autocorrelation (ACF) and partial autocorrelation (PACF) functions showed a slow hyperbolic decay indicating the presence of long memory. In the present price series, long memory parameter is found to be significant. On the basis of minimum AIC values, the best model is identified. To this end, evaluation of forecasting is carried out with root mean squares prediction error (RMSPE), mean absolute prediction error (MAPE) and relative mean absolute prediction error (RMAPE). The residuals of the fitted models were used for diagnostic checking. It is found that ARFIMA model has been able to capture the long memory present in the data set. The R software package has been used for data analysis.