A Stochastic Frontier Analysis Approach for Estimating Energy Demand and Efficiency in the Transport Sector of Latin America and the Caribbean
Manuel Llorca (),
José Somoza and
The Energy Journal, 2017, vol. Volume 38, issue Number 5
In this paper, a stochastic frontier analysis approach is applied to estimate energy demand functions in the transport sector. This approach allows us to obtain energy efficiency measures at country level that are a robust alternative to the energy intensity indicators commonly used for international comparisons. A transitive multilateral price index is constructed for aggregating the diverse energy components employed in the sector. Due to the likely unobserved heterogeneity among countries, the use of a random parameters model is proposed to accommodate these differences and to obtain different income and price elasticities per country. The estimated model is compared with alternative approaches of addressing this issue such as latent class, true fixed effects or true random effects models. This study is the first to use a random parameters stochastic frontier approach in the estimation of energy demand functions. The proposed procedure is applied to Latin America and the Caribbean, where the transport sector represents a large share of total energy consumption.
JEL-codes: F0 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to IAEE members and subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:aen:journl:ej38-5-llorca
Ordering information: This journal article can be ordered from
Access Statistics for this article
More articles in The Energy Journal from International Association for Energy Economics Contact information at EDIRC.
Bibliographic data for series maintained by David Williams ().