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Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
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:
3
Seitenangaben Beitrag:
523-534
Sprache:
en
Volltext / DOI:
doi:10.1080/10618600.2020.1725523
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
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