Unobserved Components Model for Forecasting Sugarcane Yield in Haryana
Open Access
- 10 September 2019
- journal article
- Published by ANSF Publications in Journal of Applied and Natural Science
- Vol. 11 (3), 661-665
- https://doi.org/10.31018/jans.v11i3.2144
Abstract
Unlike classical regression analysis, the state space models have time-dependent parameters and provide a flexible class of dynamic and structural time series models. The unobserved component model (UCM) is a special type of state space models widely used to analyze and forecast time series. The present investigation has been carried out to study the trend of sugarcane(gur) yield in five districts (Ambala, Karnal, Panipat, Yamunanagar and Kurukshetra) of Haryana state using the unobserved component models with level, trend and irregular components. For this purpose, the time series data on sugarcane yield from 1966-67 to 2016-17 of Ambala and Karnal, 1971-72 to 2016-17 of Kurukshetra and 1980-81 to 2016-17 of Panipat and Yamunanagar districts have been used. For all the districts, the irregular component was found to be highly significant (p=0.01) while both level and trend component variances were observed non-significant. Significance analysis of the individual component(s) has also been performed for possible dropping of the level and trend components by setting their variances equal to zero. The state space models may be effectively used pertaining to Indian agriculture data, as it takes into account the time dependency of the underlying parameters which may further enhance the predictive accuracy of the most popularly used ARIMA models with parameter constancy. Moreover, the unobserved component model is capable of handling both stationary as well as non-stationary time series and thus found more suitable for sugarcane yield modeling which is a trended yield (i.e. non-stationary in nature).Keywords
This publication has 4 references indexed in Scilit:
- State space modelling and forecasting of sugarcane yield in Haryana, IndiaJournal of Applied and Natural Science, 2017
- Forecasting Sugarcane Yield of Tamilnadu Using ARIMA ModelsSugar Tech, 2011
- State Space Modeling of Time SeriesPublished by Springer Science and Business Media LLC ,1987
- Canonical Correlation Analysis of Time Series and the Use of an Information CriterionPublished by Elsevier BV ,1976