Arbeitspapier
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: CFS Working Paper ; No. 450
- Klassifikation
-
Wirtschaft
Estimation: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Econometric Modeling: General
- Thema
-
Multi-Step estimation
Sparse estimation
Multivariate time series
Maximum likelihood estimation
Copula
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Hautsch, Nikolaus
Okhrin, Ostap
Ristig, Alexander
- Ereignis
-
Veröffentlichung
- (wer)
-
Goethe University Frankfurt, Center for Financial Studies (CFS)
- (wo)
-
Frankfurt a. M.
- (wann)
-
2014
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Hautsch, Nikolaus
- Okhrin, Ostap
- Ristig, Alexander
- Goethe University Frankfurt, Center for Financial Studies (CFS)
Entstanden
- 2014
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