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. Analyzing price dynamics 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 operate daily or weekly. In this paper, we investigate the relevance of data frequency in price transmission analysis. We use a standard- and a threshold vector error model to estimate and compare price adjustment parameters for a high frequency, semi-weekly, data and a low frequency, monthly data. 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. We suspect that using low frequency data leads to an overestimation of price adjustment parameters. The findings therefore confirm observations in the literature that high frequency data is capable of estimating price adjustment parameters more precisely than low frequency data. More research involving a large number of observations is however needed to enhance our learning from the usefulness of high frequency data in price transmission analysis.