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Efficient Iterative Maximum Likelihood Estimation of High-Parameterized Time Series Models

Nikolaus Hautsch (), Ostap Okhrin and Alexander Ristig

SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649

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.

Keywords: Multi-Step estimation; Sparse estimation; Multivariate time series; Maximum likelihood estimation; Copula (search for similar items in EconPapers)
JEL-codes: C13 C32 C50 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2014-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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Working Paper: Efficient iterative maximum likelihood estimation of high-parameterized time series models (2014) Downloads
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