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Bayesian inference for a semi-parametric copula-based Markov chain
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Azam, Kazim and Pitt, Michael K. (2014) Bayesian inference for a semi-parametric copula-based Markov chain. Working Paper. Coventry: University of Warwick. Department of Economics. Warwick economics research papers series (TWERPS), Volume 2014 (Number 1051). (Unpublished)
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Official URL: http://www2.warwick.ac.uk/fac/soc/economics/resear...
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 first time in time series models with specified marginal distribution, a non-parametric specification 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.
| Item Type: | Working or Discussion Paper (Working Paper) | ||||
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| Subjects: | Q Science > QA Mathematics | ||||
| Divisions: | Faculty of Social Sciences > Economics | ||||
| Library of Congress Subject Headings (LCSH): | Markov processes, Time-series analysis, Copulas (Mathematical statistics), Bayesian statistical decision theory | ||||
| Series Name: | Warwick economics research papers series (TWERPS) | ||||
| Publisher: | University of Warwick. Department of Economics | ||||
| Place of Publication: | Coventry | ||||
| Official Date: | July 2014 | ||||
| Dates: |
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| Volume: | Volume 2014 | ||||
| Number: | Number 1051 | ||||
| Number of Pages: | 25 | ||||
| Status: | Not Peer Reviewed | ||||
| Publication Status: | Unpublished | ||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||
| Date of first compliant deposit: | 28 July 2016 | ||||
| Date of first compliant Open Access: | 28 July 2016 |
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