Author(s):

Suman Verma

Abstract: Time series modeling using Autoregressive Integrated Moving Average (ARIMA) and state space (SS) models, was developed for individual univariate series of maize yield in India. In ARIMA modeling, 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 SS procedures by the application of Kalman filtering technique. The aim of this study was to evaluate univariate time series methods to forecast the maize yield in India. ARIMA (0, 1, 1) model was found to be appropriate but the SS model with lower error metrics showed the superiority over ARIMA model for this empirical study. The performances of the models were validated by comparing with the observed values.

Pages: 1695-1700  |  1136 Views  499 Downloads

Download (703KB)