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A Minimum Contrast Estimation for Spectral Densities of Multivariate Time Series

In: Research Papers in Statistical Inference for Time Series and Related Models

Author

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  • Yan Liu

    (Waseda University)

Abstract

We propose a minimum contrast estimator for multivariate time series in the frequency domain. This extension has not been thoroughly investigated, although the minimum contrast estimator for univariate time series has been studied for a long time. The proposal in this paper is based on the prediction errors of parametric time series models. The properties of the proposed contrast estimation function are explained in detail. We also derive the asymptotic normality of the proposed estimator and compare the asymptotic variance with the existing results. The asymptotic efficiency of the proposed minimum contrast estimation is also considered. The theoretical results are illustrated by some numerical simulations.

Suggested Citation

  • Yan Liu, 2023. "A Minimum Contrast Estimation for Spectral Densities of Multivariate Time Series," Springer Books, in: Yan Liu & Junichi Hirukawa & Yoshihide Kakizawa (ed.), Research Papers in Statistical Inference for Time Series and Related Models, chapter 0, pages 325-342, Springer.
    • Handle: RePEc:spr:sprchp:978-981-99-0803-5_12
    DOI: 10.1007/978-981-99-0803-5_12
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