Analysis of tea auction prices using non-cointegration based techniques

Abstract

Favorable product prices act as a set off for any commercial activity normally and perhaps its sustainability. Tea is a significant commercial crop in Kenya and has contributed immensely to the economy’s Gross Domestic Product (GDP) through its export earnings. However, tea industry has gone through many phases of ups and downs, particularly in terms of its export performance recently. Therefore, there is need to study the behavior of tea auction prices to get a deeper perspective into the behavior of the tea prices and to develop a model that is suitable to forecast the tea auction prices precisely. The study aimed at analyzing the trend of tea auction prices in Kenya, fit a suitable model for forecasting tea auction prices and finally, forecast the future tea prices using the most optimal model. . The findings of this study will therefore inform the government and other policy makers in terms of its policy formulation regarding the tea sector in order to accord it a competitive position in the global arena. The study used univariate and multivariate forecasting techniques which do not impose co-integration restrictions such as the Autoregressive Integrated Moving Average (ARIMA) and the Vector Autoregressive (VAR).The techniques used were chosen because of their flexibility, wide acceptability and they are also easy to utilize. The study used secondary data for the Mombasa Tea Auction Centre for a period of 2009 to 2018. Augmented Dicker-Fuller (ADF) test was performed for unit root tests to check for stationarity of the price series. ACF and PACF functions were estimated to assist in deciding the most appropriate orders of AR (p) and MA (q) models. AIC test was used because were considering several ARIMA models and the model with the lowest AIC was chosen. VAR model showed a high forecasting error with MAPE of 85.64% compared to that ARIMA of 9.2% for tea prices. ARIMA model performed much better than VAR model because of its high forecasting accuracy.