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.
ARIMA, FCAR, Forecasting, Stationarity
Ayekple, Y.E., Harris, E., Frempong, N.K. and Amevialor, J. (2015). Time Series Analysis of the Exchange Rate of the Ghanaian Cedi to the American Dollar. Journal of Mathematics Research. 7(3): 46-53.
Bharadwaj, S.P., Paul, R.K., Singh, D.R. and Singh, K.N. (2014). An Empirical Investigation of Arima and Garch Models in Agricultural Price Forecasting. Economic Affairs. 59 (3): 415-428.
Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (2007). Time-Series Analysis: Forecasting and Control. Pearson Edu-cation. India.
Cai, Z., Fan, J. and Yao, Q. (2000). Functional-coefficient regression models for nonlinear time-series. Journal of American Statistical Association. 95: 941–956.
Cai, Z., Li, Q. and Park, J.Y. (2009). Functional-coefficient models for nonstationary time series data. Journal of Econometrics. 148: 101-113.
Chen, R. and Tsay, R. S. (1993). Functional coefficient auto-regressive models. Journal of American Statistical As-sociation. 88: 298-308.
Chuang, W.I., Liu, H.H. and Susmel, R. (2012). The bivari-ate GARCH approach to investigating the relation be-tween stock returns, trading volume, and return volatil-ity. Global Finance Journal. 23 (1): 1-15.
Fan, J. and Yao, Q. (2003). Nonlinear Time-Series: Nonpara-metric and Parametric Methods. Springer, U.S.A.
Ghosh, H., Paul, R. K. and Prajneshu. (2010). Functional coefficient autoregressive model for forecasting Indian lac export data. Model Assisted Statistics and Applica-tions. 5 (2): 101-108.
Gupta, R and Basu, P. K. (2007). Weak form efficiency in Indian stock markets. International Business and Eco-nomics Research Journal. 6 (3): 57-64.
Hassan, M.F., Islam, M.A., Imam, M.F. and Sayem, S.M. (2013). Forecasting wholesale price of coarse rice in Bangladesh: A seasonal autoregressive integrated mov-ing average approach. Journal of the Bangladesh Agri-cultural University. 11(2): 271-276.
Jha, G.K. and Sinha, K. (2013). Agricultural price forecast-ing using neural network model: An innovative infor-mation delivery system. Agricultural Economics Re-search Review. 26 (2): 229-239.
Makridakis, S., Wheelright, S.C. and Hyndman, R.J. (2003). Forecasting: Methods and Applications. Wiley-India. New Delhi.
Padhan, P. C. (2012). Application of ARIMA model for forecasting agricultural productivity in India, Journal of Agriculture & Social Sciences. 8: 50-56.
Paul, R. K. (2010). Stochastic Modeling of Wholesale Prices of Rohu in West Bengal, India. Interestat.
Paul, R. K. and Das, M. K. (2013). Forecasting of average annual fish landing in Ganga Basin. Fishing chimes. 33 (3): 51-54.
Paul, R. K., Alam, W. and Paul, A. K. (2014). Prospects of livestock and dairy production in India under time series framework. Indian Journal of Animal Sciences. 84 (4): 130-134.
Paul, R.K., Panwar, S., Sarkar, S.K., Kumar, A., Singh, K.N., Farooqi, S. and Choudhary, V.K. (2013b). Model-ling and forecasting of meat exports from India. Agri-cultural Economics Research Review. 26 (2): 249-255.
Paul, R.K., Prajneshu and Ghosh, H. (2013a). Statistical modelling for forecasting of wheat yield based on weather variables. Indian Journal of Agricultural Sci-ences. 83 (2): 180-183.
Prabakaran, K., Sivapragasam, C., Jeevapriya, C. and Nar-matha, A. (2013). Forecasting cultivated areas and pro-duction of wheat in India using ARIMA model, Golden Research Thoughts. 3 (3).
Su, J. and Deng, G. (2014). The Chinese urban and rural per capita income and trend analysis. Applied Mathematics, 5: 106-109.
Tan, S., Bhaduri, M. and Ho, C.H. (2014). A Statistical Model for Long-Term Forecasts of Strong Sand Dust Storms. Journal of Geoscience and Environment Pro-tection. 2:16-26.
Toumache, R., Rouaski, K. and Talbi, B. (2014). The impact of fluctuating oil prices on inflation in Algeria. Journal of Business and Retail Management Research, 9 (1): 64-72.
Tsay, R. S. (2010). Analysis of Financial Time series. John Wiley and Sons, New Jersey.
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