Copula-Based Dynamic Models for Multivariate Time Series

2018 ◽  
Author(s):  
Bouchra R. Nasri ◽  
Bruno Remillard
Keyword(s):  
Time Series ◽  
Dynamic Models ◽  
Multivariate Time Series

Copula-based dynamic models for multivariate time series

2019 ◽  
Vol 172 ◽  
pp. 107-121 ◽  
Author(s):  
Bouchra R. Nasri ◽  
Bruno N. Rémillard
Keyword(s):  
Time Series ◽  
Dynamic Models ◽  
Multivariate Time Series

scholarly journals USING SUBSPACE METHODS FOR ESTIMATING ARMA MODELS FOR MULTIVARIATE TIME SERIES WITH CONDITIONALLY HETEROSKEDASTIC INNOVATIONS

2008 ◽  
Vol 24 (4) ◽  
pp. 1063-1092 ◽  
Author(s):  
Dietmar Bauer
Keyword(s):  
Time Series ◽  
Dynamic Models ◽  
Moving Average ◽  
Multivariate Time Series ◽  
Innovation Process ◽  
Stationary Time Series ◽  
Autoregressive Moving Average ◽  
Subspace Methods ◽  
Conditional Mean ◽  
Quasi Maximum Likelihood

This paper deals with the estimation of linear dynamic models of the autoregressive moving average type for the conditional mean for stationary time series with conditionally heteroskedastic innovation process. Estimation is performed using a particular class of subspace methods that are known to have computational advantages as compared to estimation based on criterion minimization. These advantages are especially strong for high-dimensional time series. Conditions to ensure consistency and asymptotic normality of the subspace estimators are derived in this paper. Moreover asymptotic equivalence to quasi maximum likelihood estimators based on the Gaussian likelihood in terms of the asymptotic distribution is proved under mild assumptions on the innovations. Furthermore order estimation techniques are proposed and analyzed.

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