 Bayesian multivariate nonlinear state space copula modelsAlexander Kreuzer, Luciana Dalla Valle and Claudia Czado Computational Statistics & Data Analysis, 2023, vol. 188, issue C Abstract: A novel flexible class of multivariate nonlinear non-Gaussian state space models, based on copulas, is proposed. Specifically, it is assumed that the observation equation and the state equation are defined by copula families that are not necessarily equal. Inference is performed within the Bayesian framework, using the Hamiltonian Monte Carlo method. Simulation studies show that the proposed copula-based approach is extremely flexible, since it is able to describe a wide range of dependence structures and, at the same time, allows us to deal with missing data. The application to atmospheric pollutant measurement data shows that the approach is suitable for accurate modeling and prediction of data dynamics in the presence of missing values. Comparison to a Gaussian linear state space model and to Bayesian additive regression trees shows the superior performance of the proposed model with respect to predictive accuracy. Keywords: Bayesian inference; Copulas; Hamiltonian Monte Carlo; State space models (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:188:y:2023:i:c:s0167947323001317 DOI: 10.1016/j.csda.2023.107820 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.