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Manfred Deistler and the General Dynamic Factor Model Approach to the Analysis of High-Dimensional Time Series
Version 1
: Received: 27 September 2022 / Approved: 28 September 2022 / Online: 28 September 2022 (02:02:26 CEST)
A peer-reviewed article of this Preprint also exists.
Hallin, M. Manfred Deistler and the General-Dynamic-Factor-Model Approach to the Statistical Analysis of High-Dimensional Time Series. Econometrics 2022, 10, 37. Hallin, M. Manfred Deistler and the General-Dynamic-Factor-Model Approach to the Statistical Analysis of High-Dimensional Time Series. Econometrics 2022, 10, 37.
Abstract
For more than half a century, Manfred Deistler has been contributing to the construction of the rigorous theoretical foundations of the statistical analysis of time series and more general stochastic processes. Half a century of unremitting activity is not easily summarized in a few pages. In this short note, we chose to concentrate on a relatively little-known aspect of Manfred's contribution which nevertheless had quite an impact on the development of one of the most powerful tools of contemporary time series and econometrics: dynamic factor models.
Keywords
High-dimensional time series; General Dynamic Factor Models; spiked covariance model; reduced-rank process; singular spectrum.
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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