Identification of Generalized Dynamic Factor Models from mixed-frequency data

B. D.O. Anderson, Alexander Braumann, Manfred Deistler

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Modeling of high dimensional time series by linear time series models such as vector autoregressive models is often marred by the so-called “curse of dimensionality”. In order to overcome this problem generalized linear dynamic factor models (GDFM's) maybe used. In high-dimensional time series the single univariate time series are often sampled at different frequencies. This is the so-called mixed-frequency situation. We consider identifiability of the underlying high-frequency GDFM (i.e. the GDFM generating the data at the highest sampling frequency occurring) in the case of mixed frequency data and we shortly describe two estimation procedures in this situation based on the EM algorithm.

Original languageEnglish
Pages (from-to)1008-1013
Number of pages6
Journal18th IFAC Symposium on System Identification SYSID 2018: Stockholm, Sweden, 9-11 July 2018
Volume51
Issue number15
DOIs
Publication statusPublished - 1 Jan 2018
Externally publishedYes

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