Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/91592
Authors: 
Hautsch, Nikolaus
Okhrin, Ostap
Ristig, Alexander
Year of Publication: 
2014
Series/Report no.: 
SFB 649 Discussion Paper 2014-010
Abstract: 
We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields computationally tractable, consistent and asymptotic efficient estimates of all parameters. We show the asymptotic normality and derive the estimator's asymptotic covariance in dependence of the number of iteration steps. To mitigate the curse of dimensionality in high-parameterized models, we combine the procedure with a penalization approach yielding sparsity and reducing model complexity. Small sample properties of the estimator are illustrated for two time series models in a simulation study. In an empirical application, we use the proposed method to estimate the connectedness between companies by extending the approach by Diebold and Yilmaz (2014) to a high-dimensional non-Gaussian setting.
Subjects: 
Multi-Step estimation
Sparse estimation
Multivariate time series
Maximum likelihood estimation
Copula
JEL: 
C13
C32
C50
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.