Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/91592 
Erscheinungsjahr: 
2014
Schriftenreihe/Nr.: 
SFB 649 Discussion Paper No. 2014-010
Verlag: 
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, Berlin
Zusammenfassung: 
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.
Schlagwörter: 
Multi-Step estimation
Sparse estimation
Multivariate time series
Maximum likelihood estimation
Copula
JEL: 
C13
C32
C50
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.