Study on onion price forecast using time series methods

Abstract

Agricultural production is characterized by risks and uncertainties arising largely due to uncertain yields and relatively low price elasticity of demand, of most commodities. Commodity price movements have a major impact on overall macroeconomic performance. Hence, commodity-price forecasts are a key input to macroeconomic policy planning and formulation. The price volatility in case of Onion is considered to be well known in India. This study has been undertaken to forecast Onion prices before the crop arrival and particularly in the lean periods which witnesses high rise in Onion price. The administration may find enough time period to readjust supply position of Onions in order at avoid high price situation. The study has been illustrated with the time series data on daily Spot price of Onion in Delhi Azadpur Market from 01 January 2009 to 30September 2012. This study was undertaken to obtain a suitable forecast model for forecasting Onion prices. ARIMA (1, 1, 2) model gives reasonable and acceptable forecasts; it does not perform well when there existed volatility in the data series. In this study, GARCH (1, 1) has also been used to forecast prices. The model performs better than ARIMA (0, 1, 1) because of its ability to capture the volatility by the conditional variance of being non-constant throughout the time. Vector Auto Regressive (VAR) a multivariate model for forecast was also attempted but the performance of the model was not improved over GARCH model. The GARCH (1,1) was concluded to be a better model than others in forecasting price of Onion because the values for test statistics calculated using this model were smaller than those calculated using other model and also both the AIC and SIC values from GARCH model were smaller and the percent deviation in forecast price from actual price was comparatively low in GARCH model. Therefore, it showed that GARCH is a better model than ARIMA for estimating daily prices.