Nonlinear price transmission in the rice market in Senegal: a model-based recursive partitioning approach
Fousseini Traoré,
Suwadu Sakho Jimbira and
Leysa Sall
Applied Economics, 2022, vol. 54, issue 20, 2343-2355
Abstract:
This paper analyzes the nonlinear effects in price transmission from international markets to the local rice market of Dakar (Senegal) focusing on asymmetries through threshold effects. We use recent machine learning methods (model-based recursive partitioning trees) to detect asymmetries in the price transmission mechanism. Using a model based recursive partitioning algorithm, we identify a threshold and confirm the asymmetry in the price transmission. Local retail prices are more sensitive to world price increases than to declines. Only 11.80% of positive deviations (international prices go down) are eliminated at the end of the subsequent month, while 39.50% of negative deviations (world prices go up) are eliminated after one month. These results highlight the role of transaction costs and the market power of commercial intermediaries in price transmission in the sense that margins are corrected more rapidly when they are squeezed relative to their long run level than when they are stretched. Our results are confirmed by the traditional Threshold Autoregressive (TAR) model.
Date: 2022
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/00036846.2021.1989369 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:applec:v:54:y:2022:i:20:p:2343-2355
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20
DOI: 10.1080/00036846.2021.1989369
Access Statistics for this article
Applied Economics is currently edited by Anita Phillips
More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().