Exact Discrete Representations of Linear Continuous Time Models with Mixed Frequency Data

Research output: Contribution to journalArticlepeer-review

Abstract

The time aggregation of vector linear processes containing (i) mixed stock-
ow data and (ii) aggregated at mixed frequencies, is explored, focusing on a method to translate the parameters of the underlying continuous time model into those of an equivalent model of the observed data. Based on manipulations of a general state-space form, the results may be used to model multiple frequencies or aggregation schemes. Estimation of the continuous
time parameters via the ARMA representation of the observable data vector is discussed and demonstrated in an application to model stock price and dividend data. Simulation evidence suggests that these estimators have superior properties to the traditional approach of concentrating the data to a single low frequency.
Original languageEnglish
Article number5
Pages (from-to)951-967
Number of pages17
JournalJournal of Time Series Analysis
Volume40
Issue number6
Early online date6 May 2019
DOIs
Publication statusE-pub ahead of print - 6 May 2019

Bibliographical note

© 2019 John Wiley & Sons Ltd. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

Keywords

  • Time aggregation
  • CARMA process
  • mixed frequency
  • State space model
  • exact discrete representation
  • the exact discrete representation
  • state space

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