UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Importance sampling from posterior distributions using copula-base approximations

Dellaportas, P; Tsionas, MG; (2019) Importance sampling from posterior distributions using copula-base approximations. Journal of Econometrics , 210 (1) pp. 45-57. 10.1016/j.jeconom.2018.11.004.

[img] Text
copulas_final.pdf - Accepted version
Access restricted to UCL open access staff until 13 November 2020.

Download (541kB)

Abstract

We provide generic approximations to k-dimensional posterior distributions through an importance sampling strategy. The importance function is a product of k univariate of Student-t densities and a k-dimensional betaLiouville density. The parameters of the densities and the number of components in the mixtures are adaptively optimized along the Monte Carlo sampling. For challenging high dimensional latent Gaussian models we propose a nested importance function approximation. We apply the techniques to a range of econometric models that have appeared in the literature, and we document their satisfactory performance relative to the alternatives.

Type: Article
Title: Importance sampling from posterior distributions using copula-base approximations
DOI: 10.1016/j.jeconom.2018.11.004
Publisher version: https://doi.org/10.1016/j.jeconom.2018.11.004
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1571024
Downloads since deposit
2Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item