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Dokumenttyp:
Zeitschriftenaufsatz 
Autor(en):
Alexander Kreuzer, Claudia Czado 
Titel:
Efficient Bayesian inference for nonlinear state space models with univariate autoregressive state equation 
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...    »
 
Stichworte:
Asymmetric tail dependence; Copulas; Financial time series; Time varying parameters; Volatility 
Dewey Dezimalklassifikation:
510 Mathematik 
Zeitschriftentitel:
Journal of Computational and Graphical Statistics 
Jahr:
2020 
Band / Volume:
29 
Jahr / Monat:
2020-03 
Quartal:
1. Quartal 
Monat:
Mar 
Heft / Issue:
Seitenangaben Beitrag:
523-534 
Sprache:
en 
Verlag / Institution:
Taylor & Francis 
Hinweise:
Published online: 12 Mar 2020 
Status:
Erstveröffentlichung 
Angenommen (von Zeitschrift):
27.01.2020 
Publikationsdatum:
10.03.2020 
Semester:
WS 19-20 
TUM Einrichtung:
Professur für Angewandte Mathematische Statistik 
Format:
Text