Efficient iterative maximum likelihood estimation of high-parameterized time series models

  • 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.

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Metadaten
Author:Nikolaus Hautsch, Ostap Okhrin, Alexander Ristig
URN:urn:nbn:de:hebis:30:3-331463
Parent Title (German):Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 450
Series (Serial Number):CFS working paper series (450)
Publisher:Center for Financial Studies
Place of publication:Frankfurt, M.
Document Type:Working Paper
Language:English
Date of Publication (online):2014/01/27
Date of first Publication:2014/01/27
Publishing Institution:Universit├Ątsbibliothek Johann Christian Senckenberg
Release Date:2014/02/28
Tag:Copula; Maximum likelihood estimation; Multi-Step estimation; Multivariate time series; Sparse estimation
HeBIS-PPN:349979014
Institutes:Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
JEL-Classification:C Mathematical and Quantitative Methods / C1 Econometric and Statistical Methods: General / C13 Estimation
C Mathematical and Quantitative Methods / C3 Multiple or Simultaneous Equation Models / C32 Time-Series Models; Dynamic Quantile Regressions (Updated!)
C Mathematical and Quantitative Methods / C5 Econometric Modeling / C50 General
Sammlungen:Universit├Ątspublikationen
Licence (German):License LogoDeutsches Urheberrecht