Bookmark and Share

An analysis of count data models for the study of exclusivity in wine consumption

[journal article]

Cano Fernández, Victor Javier; Guirao Pérez, Ginés; Rodriguez Donate, Maria Carolina; Romero Rodriguez, Margartita Esther

fulltextDownloadDownload full text

(337 KByte)

Citation Suggestion

Please use the following Persistent Identifier (PID) to cite this document:http://nbn-resolving.de/urn:nbn:de:0168-ssoar-241183

Further Details
Abstract Several models which analyze count data have been proposed in econometric literature. These models allow the discrete, non-negative nature of specific phenomena of interest to be gathered in a appropriate way and can be useful for the explanation of specific preference structures among individuals. In this work, an analysis of the number of wine types consumed by residents of Tenerife is carried out, with an aim to observe which characteristics determine the exclusivity in its consumption, given the current context of increased competition in this sector. The specific characteristics of the considered variable allow the study to cover two aspects. The first is methodological, and is seen by the variety of models that may be considered in this case. This focus consists in comparing several possibilities which fit the type of count data involved. The second aspect is clearly empirical, and is based on the description of not only the most appropriate decision-making mechanism for the study but in the identification of those factors that explain the diversity in wine consumption.
Classification Economic Sectors; Economic Statistics, Econometrics, Business Informatics
Free Keywords count data models; poisson regression model; negative binomial regression models; finite mixture models; number of wine types
Document language English
Publication Year 2009
Page/Pages p. 1563-1574
Journal Applied Economics, 41 (2009) 12
DOI http://dx.doi.org/10.1080/00036840601032227
Status Postprint; peer reviewed
Licence PEER Licence Agreement (applicable only to documents from PEER project)