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Displaying results 1 to 3 of 3.

  1. Efficient iterative maximum likelihood estimation of high-parameterized time series models
    Author: Hautsch, Nikolaus; Okhrin, Ostap; Ristig, Alexander
    Published: 2014
    Publisher:  SFB 649, Economic Risk, Berlin

    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... more

    Location:
    Staats- und Universitätsbibliothek Bremen
    Inter-library loan:
    No inter-library loan

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 86 (2014,10)
    Inter-library loan:
    No inter-library loan

     

    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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    Content information
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/91592
    Series: SFB 649 discussion paper ; 2014-010
    Subjects: Multi-Step estimation; Sparse estimation; Multivariate time series; Maximum likelihood estimation; Copula
    Scope: Online-Ressource (32 S.), graph. Darst.
  2. Efficient iterative maximum likelihood estimation of high-parameterized time series models
    Author: Hautsch, Nikolaus; Okhrin, Ostap; Ristig, Alexander
    Published: 2014
    Publisher:  Center for Financial Studies, Frankfurt, Main

    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... more

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 108 (450)
    Inter-library loan:
    No inter-library loan

     

    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.

     
    Export to reference management software   RIS file
      BibTeX file
    Content information
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/92941
    Series: CFS working paper ; 450
    Subjects: Multi-Step estimation; Sparse estimation; Multivariate time series; Maximum likelihood estimation; Copula
    Scope: Online-Ressource (32 S.), graph. Darst.
  3. Estimation procedures for exchangeable Marshall copulas with hydrological application
    Author: Durante, Fabrizio; Okhrin, Ostap
    Published: 2014
    Publisher:  SFB 649, Economic Risk, Berlin

    Complex phenomena in environmental sciences can be conveniently represented by several inter-dependent random variables. In order to describe such situations, copula-based models have been studied during the last year. In this paper, we consider a... more

    Location:
    Staats- und Universitätsbibliothek Bremen
    Inter-library loan:
    No inter-library loan

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 86 (2014,14)
    Inter-library loan:
    No inter-library loan

     

    Complex phenomena in environmental sciences can be conveniently represented by several inter-dependent random variables. In order to describe such situations, copula-based models have been studied during the last year. In this paper, we consider a novel family of bivariate copulas, called exchangeable Marshall copulas. Such copulas describe both positive and (upper) tail association between random variables. Specifically, inference procedures for the family of exchangeable Marshall copulas are introduced, based on the estimation of their (univariate) generator. Moreover, the performance of the proposed methodologies is shown in a simulation study. Finally, an illustration describes how the proposed procedures can be useful in a hydrological application.

     
    Export to reference management software   RIS file
      BibTeX file
    Content information
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/93224
    Series: SFB 649 discussion paper ; 2014-014
    Subjects: Copula; Kendall distribution; Marshall-Olkin distribution; Non-parametric Estimation; Risk Management
    Scope: Online-Ressource (28 S.), graph. Darst.