Bayesian Inference for a Semi-Parametric Copula-based Markov Chain

Kazim Azam and Michael Pitt
Additional contact information
Kazim Azam: Vrije Universiteit, Amsterdam
Michael Pitt: Department of Economics, University of Warwick

The Warwick Economics Research Paper Series (TWERPS) from University of Warwick, Department of Economics

Abstract: This paper presents a method to specify a strictly stationary univariate time series model with particular emphasis on the marginal characteristics (fat tailedness, skewness etc.). It is the rst time in time series models with speci ed marginal distribution, a non-parametric speci cation is used. Through a Copula distribution, the marginal aspect are separated and the information contained within the order statistics allow to efficiently model a discretely-varied time series. The estimation is done through Bayesian method. The method is invariant to any copula family and for any level of heterogeneity in the random variable. Using count times series of weekly rearm homicides in Cape Town, South Africa, we show our method efficiently estimates the copula parameter representing the first-order Markov chain transition density. JEL classification: C11 ; C14 ; C20

Keywords: Bayesian copula; discrete data; order statistics; semi-parametric; time series. (search for similar items in EconPapers)
Date: 2014
New Economics Papers: this item is included in nep-dcm and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://warwick.ac.uk/fac/soc/economics/research/w ... 51_azam_and_pitt.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:wrk:warwec:1051

Access Statistics for this paper

More papers in The Warwick Economics Research Paper Series (TWERPS) from University of Warwick, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Margaret Nash ().