Spatial price transmission analysis in Ghanaian agricultural markets: Does the data frequency improve our estimation?

Abstract

Unavailability of high frequency weekly or daily data compels most studies of price transmission in developing countries to use low frequency monthly data for their analyses. Analysing price dynamics, especially in agricultural markets, with monthly data may however yield imprecise price adjustment parameters and lead to wrong inferences on price dynamics. This is because agricultural markets in developing countries usually operate daily or weekly, not monthly, as implied by the market analysts who use low frequency data. This paper investigates the relevance of data frequency in price transmission analysis by using a standard and a threshold vector error correction model to estimate and compare price adjustment parameters for high frequency semi-weekly data and low frequency monthly data obtained from five major fresh tomato markets in Ghana. The results reveal that adjustment parameters estimated from the low frequency data are higher in all cases than those estimated from the high frequency data. There is reason to suspect that using low frequency data, as confirmed in some literature, leads to an overestimation of the price adjustment parameters. More research involving a large number of observations is however needed to enhance our knowledge about the usefulness of high frequency data in price transmission analysis.