The Log Normal and the Poisson Gravity Models in the Analysis of Interactions Phenomena
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 4, July 2015, Pages: 291-299
Received: May 28, 2015; Accepted: Jun. 9, 2015; Published: Jul. 4, 2015
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Giuseppe Ricciardo Lamonica, Department of Economics and Social Sciences, Polytechnic University of Marche, Ancona, Italy
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Three problems often encountered when bilateral interaction data are analyzed by means of the log-normal gravity model: the bias created by the logarithmic transformation, the failure of the homoscedasticity assumption and the treatment of zero valued flows. When the interaction are count data type that takes non-negative integer values, to overcome these problems the literature suggests to use a Poisson gravity model instead of log-normal model. In this paper, using a real interaction phenomenon a comparative analysis of the two models is carried out. The most important results obtained highlights that if the phenomenon is correctly specified, the two specification of the gravity model have a very similar behaviour.
Gravity Model, Poisson Model, Log Normal Model, Comparisons, Count Data
To cite this article
Giuseppe Ricciardo Lamonica, The Log Normal and the Poisson Gravity Models in the Analysis of Interactions Phenomena, American Journal of Theoretical and Applied Statistics. Vol. 4, No. 4, 2015, pp. 291-299. doi: 10.11648/j.ajtas.20150404.19
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