Box and Jenkinsâ€™ Autoregressive Integrated Moving Average (ARIMA) models are widely used for analyzing and forecasting the time-series data. In this approach, the underlying parameters are assumed to be constant however the data in agriculture are generally collected over time and thus have the time-dependency in parameters. Such data can be analyzed using state space (SS) procedures by the application of Kalman filtering technique. The purpose of this article is to illustrate the usefulness of state space models in sugarcane yield forecasting and to pro-vide some empirical evidence for its superiority over the classical time-series analysis. ARIMA and state space models individually could provide the suitable relationship(s) to reliably forecast the sugarcane yield in Karnal, Ambala, Kurukshetra, Yamunanagar and Panipat districts of Haryana (India). However, the state space models with lower error metrics showed the superiority over ARIMA models for this empirical study. The sugarcane yield forecasts based on SS models in the districts under consideration showed good agreement with State Department of Agriculture (DOA) yields by showing 3-6 percent average absolute deviations.
Autocorrelation function, Kalman filtering technique, State space procedures, Akaikeâ€™s information criterion, Sugarcane yield forecast
Akaike, H. (1976). Canonical correlations analysis of time series and the use of an information criterion in advances and case studies in system identification (R. Mehra and D.G. Lainiotis (Eds.)). Academic Press, New York.
Aoki, M. (1987). State Space modeling of time series. Springer, Berlin.
Bordoloi, S. (2009). Estimation of price level in India through state-space model. Statistics and Applications, 7&8(1&2): 17-36.
Box, G.E.P. and Jenkins, G.M. (1976). Time series analysis: Forecasting and control. Holden Day, San Franscisco.
Brockwell, P. J., Davis, R. A. (2002). Introduction to time series and forecasting. Springer, New York.
Durbin J. (2002). The Foreman Lecture: The State Space approach to time series analysis and its potential for Official Statistics. Austral. New Zealand J. Statist. 42: 1-23.
Jong, P. D. and Penzer, J. (2004). The ARMA model in state space form. Statistics and Probability letters, 70(1): 119-125.
Kitagawa, G. and Gersch, W. (1984). A smoothness priors-state space modeling of time series with trend and seasonality. J. Amer. Statist. Assoc. 79 : 378-389.
Ljung, G.M. and Box, G.E.P. (1978). On a measure of lack of fit in time series models. Biometrika, 6 : 297-303.
Marquardt, D.W. (1963). An algorithm for least-squares estimation of non-linear parameters. J. Soc. Ind. Appl. Math. 2 : 431-441.
Meinhold R.J. and Singpurwalla N.D. (1983). Understanding the kalman filter. Amer. Statist. 37 : 123-127.
Omekara, C.O., Okereke, O.E. and Ehighibe, S.E. (2016). Time series analysis of interest rate in Nigeria: A comparison of Arima and state space models. International Journal of Probability and Statistics, 5(2) : 33-47.
Pankratz, A. (1991). Forecasting with dynamic regression models. Wiley-Interscience.
Piepho, H.P. and Ogutu, J.O. (2007). Simple state-space models in a mixed model framework. Amer. Statist. 61 : 224 -232.
Saini, N and Mittal, A. K. (2014). Forecasting volatility in indian stock market using State Space models. Journal of Statistical and Econometric Methods, 3(1): 115-136.
Schwarz, G. (1978). Estimating the dimension of a model. Ann. Stat. 62 : 461- 464.
Stevenson, F.C., Knight, J.D., Wendroth, C., Kessel, V.C. and Nielsen, D.R. (2001). A comparision of two methods to predict the landscape-scale variation of crop yield. Soil and Tillage Research, 58: 163-181.
Verma, U., Goyal, A. and Goyal, M. (2015). ARIMA versus state space modelling: An application in agriculture. Adv. Appl. Res. 7 : 91-95.
Yemitan, R. A. and Shittu, O.I. (2015). Forecasting Inflation in Nigeria by state space modeling. International Journal of Scientific & Engineering Research, 6 : 778-786.
Yusof, F. and Kane, I.L. (2012). Modelling monthly rainfall time series using ETS state space and SARIMA models. International Journal of Current Research, 4 : 195-200.
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