An agricultural price forecasting model under nonstationarity using functional coefficient autoregression
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Abstract
In this globalized world, management of food security in the developing countries like India where agriculture is dominated needs efficient and reliable price forecasting models more than ever. Forecasts of agricultural prices are handy to the policymakers, agribusiness industries and farmers. In the present study, Functional Coefficient Autoregression (FCAR) has been applied for modeling and forecasting the monthly wholesale price of clean coffee seeds in Hyderabad coffee consuming center using the data from Jan, 2001 to Sep, 2014. FCAR (2,2) model was found suitable based on the minimum Average Prediction Error (APE) criterion. The FCAR model thus obtained was compared with the Autoregressive Integrated Moving Average (ARIMA) model. Since the original series was found to be nonstationary from Augmented Dickey-Fuller test (ADF statistic=-2.84, p=0.22), the differenced series (ADF statistic=-4.20, p<0.01) was used and ARIMA (12,1,0) was found suitable. The FCAR model obtained was compared with the ARIMA model with respect to forecast accuracy measures viz., Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The RMSE and MAPE for the FCAR (2,2) were found to be 17.16 and 4.41%, respectively, whereas for the ARIMA (12,1,0) models, 62.64 and 26.15%, respectively. The results indicated that the FCAR model was efficient than the ARIMA model in forecasting the future prices.
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ARIMA, FCAR, Forecasting, Stationarity
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