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Title:

Efficient Bayesian inference for nonlinear state space models with univariate autoregressive state equation

Document type:
Zeitschriftenaufsatz
Author(s):
Kreuzer, Alexander; Czado, Claudia
Abstract:
Latent autoregressive processes are a popular choice to model time varying parameters. These models can be formulated as nonlinear state space models for which inference is not straightforward due to the high number of parameters. Therefore maximum likelihood methods are often infeasible and researchers rely on alternative techniques, such as Gibbs sampling. But conventional Gibbs samplers are often tailored to specific situations and suffer from high autocorrelation among repeated draws. We pre...     »
Keywords:
Asymmetric tail dependence; Copulas; Financial time series; Time varying parameters; Volatility
Dewey Decimal Classification:
510 Mathematik
Journal title:
Journal of Computational and Graphical Statistics
Year:
2020
Journal volume:
29
Year / month:
2020-03
Quarter:
1. Quartal
Month:
Mar
Journal issue:
3
Pages contribution:
523-534
Language:
en
Fulltext / DOI:
doi:10.1080/10618600.2020.1725523
Publisher:
Taylor & Francis
Notes:
Published online: 12 Mar 2020
Status:
Erstveröffentlichung
Accepted:
27.01.2020
Date of publication:
10.03.2020
Semester:
WS 19-20
TUM Institution:
Professur für Angewandte Mathematische Statistik
Format:
Text
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