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G. MOHAN NAIDU* B. RAVINDRA REDDY, V. AMARNADH AND ADDANKI JOTHI BABU
Department of Statistics and Maths, S.V. Agricultural College, Tirupati – 517 502
Forecasting a time series is generally done by using autoregressive integrated moving average (ARIMA) models. The main drawback of ARIMA technique is that the time series should be stationary. In reality, this assumption is rarely met. The Unobserved Component Model (UCM) is a promising alternative to a ARIMA in overlapping this problem as it does not make use of the stationarity assumption. In addition, it breaks down response series into components such as trends, cycles and regression effects, which could be useful especially in forecasting the production of agricultural crops. The present study is aimed at using UCM for annual wheat area and production in India. Results revealed that both the trend components, level and slope were insignificant. The linear trend model zero variance slope was found to be the best fit for the data. The forecast error for the area and production are 2012 and 2013 were 0.43% and 2.73% while 0.28% and 0.94% respectively. From the fitted model, predicted annual wheat area and production for 2016 would be 32.11 million hectares and the 95% CI is 29.38 to 34.84 million hectares where as 99.90 million tones and the CI is 91.14 to 108.67 million tones. Thus the used of UCM is recom-mended for annual data.
Non-stochastic process, trend, UCM, Ljung and Box chi-square.