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

Joseph Amikuzuno

doi:10.4102/jef.v4i2.322

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.

Highlights

Spatial price transmission or market integration (MI) measures the degree to which markets in geographically separated locations share common long-run price or trade information on a homogeneous commodity
The Johansen’s maximum likelihood (ML) cointegration test was used to determine the number of cointegrating vectors between the market pairs
Informed trade paradigms and arbitrage processes in agricultural markets, even in developing countries, signify that markets occur daily or once in a market week of three or six days. This notwithstanding, most studies of agricultural price dynamics in developing countries are based on low frequency monthly prices instead of high frequency daily or weekly prices

Summary

Introduction

Spatial price transmission or market integration (MI) measures the degree to which markets in geographically separated locations share common long-run price or trade information on a homogeneous commodity. Techniques for analysing market integration are quite sophisticated, but most empirical studies that use sophisticated techniques to analyse spatial price transmission in agricultural markets suffer from a common drawback – the failure to use data of relevant frequency for their analyses. The agricultural market integration literature on developing countries indicates a common trend by a majority of studies using low frequency quarterly or monthly data to investigate market performance. The unavailability of reliable high frequency and complete (daily or weekly) data from secondary sources is often the excuse for not using this form of data for investigating price integration in the agricultural markets of developing countries. Agricultural markets in developing countries are usually widely dispersed, implying exorbitant associated costs in collecting high frequency data (HFD) and compelling researchers to collect and use low frequency, quarterly or monthly market data

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