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

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 firearm homicides in Cape Town, South Africa, we show our method efficiently estimates the copula parameter representing the first-order Markov chain transition density.

Issue Date:
Jul 09 2014
Publication Type:
Working or Discussion Paper
Record Identifier:
Total Pages:
JEL Codes:
C11; C14; C20
Series Statement:
WERP 1051

 Record created 2018-03-29, last modified 2018-03-29

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