On discriminating between lognormal and Pareto tail: A mixture-based approach

Marco Bee

No 2020/9, DEM Working Papers from Department of Economics and Management

Abstract: A large literature deals with the problem of testing for a Pareto tail and estimating the parameters of the Pareto distribution. We first review the most widely used statistical tools and identify their weaknesses. Then we develop a methodology that exploits all the available information by taking into account the data generating process of the entire population. Accordingly, we estimate a lognormal-Pareto mixture via the EM algorithm and the maximization of the profile likelihood function. Simulation experiments and an empirical application to the size of the US metropolitan areas confirm that the proposed method works well and outperforms two commonly used techniques.

Keywords: Mixture distributions; EM algorithm; lognormal distribution; Pareto distribution (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.economia.unitn.it/alfresco/download/wo ... 395b7/DEM2020_09.pdf (application/pdf)

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:trn:utwprg:2020/9

Access Statistics for this paper

More papers in DEM Working Papers from Department of Economics and Management Contact information at EDIRC.
Bibliographic data for series maintained by roberto.gabriele@unitn.it ().