Multivariate CARMA processes, continuous-time state space models and complete regularity of the innovations of the sampled processes

DOI10.3150/10-BEJ329zbMath1248.60039arXiv1203.0131OpenAlexW3101983062MaRDI QIDQ408083

Eckhard Schlemm, Robert Stelzer

Publication date: 29 March 2012

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1203.0131

zbMATH Keywords

samplingstrong mixingstate space representationcomplete regularitylinear innovationsmultivariate CARMA processvector ARMA process

Mathematics Subject Classification ID

Stationary stochastic processes (60G10)

Related Items (13)

Whittle estimation for continuous-time stationary state space models with finite second moments ⋮ Nonparametric regression for locally stationary random fields under stochastic sampling design ⋮ Cointegration in continuous time for factor models ⋮ Limit Theory for High Frequency Sampled MCARMA Models ⋮ Cointegrated continuous-time linear state-space and MCARMA models ⋮ Continuous-time locally stationary time series models ⋮ Multivariate continuous-time autoregressive moving-average processes on cones ⋮ Quasi maximum likelihood estimation for strongly mixing state space models and multivariate Lévy-driven CARMA processes ⋮ Asymptotic moving average representation of high-frequency sampled multivariate CARMA processes ⋮ \(\ell_1\)-symmetric vector random fields ⋮ Factorization and discrete-time representation of multivariate CARMA processes ⋮ Quasi-maximum likelihood estimation for cointegrated continuous-time linear state space models observed at low frequencies ⋮ Empirical spectral processes for stationary state space models

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