Journal article
Modeling longitudinal data using a pair-copula decomposition of serial dependence
M Smith, A Min, C Almeida, C Czado
Journal of the American Statistical Association | Published : 2010
Abstract
Copulas have proven to be very successful tools for the flexible modeling of cross-sectional dependence. In this paper we express the dependence structure of continuous-valued time series data using a sequence of bivariate copulas. This corresponds to a type of decomposition recently called a "vine" in the graphical models literature, where each copula is entitled a "pair-copula." We propose a Bayesian approach for the estimation of this dependence structure for longitudinal data. Bayesian selection ideas are used to identify any independence paircopulas, with the end result being a parsimonious representation of a time-inhomogeneous Markov process of varying order. Estimates are Bayesian mo..
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Awarded by Australian Research Council
Funding Acknowledgements
Michael Smith is Professor of Management, Melbourne Business School, University of Melbourne, 200 Leicester Street, Carlton, VIC, 3053, Australia (E-mail: mike.smith@mbs.edu). Aleksey Min is Postdoctoral Fellow, Carlos Almeida is Postdoctoral Fellow, and Claudia Czado is Chair of Mathematical Statistics, Zentrum Mathematik, Technische Universitat Munchen, 85748 Garching, Germany. The work of Michael Smith was partially supported by Australian Research Council grant DP0985505. Claudia Czado and Carlos Almeida gratefully acknowledge the financial support from the Deutsche Forschungsgemeinschaft (Cz 86/1-3: Statistical inference for high dimensional dependence models using pair-copulas). The authors thank three referees and associate editor, all of whom made comments that improved the paper.