Generalized binary vector autoregressive processes

Artikel

Vector‐valued‐60 extensions of univariate generalized binary auto‐regressive (gbAR) processes are proposed that enable the joint modeling of serial and cross‐sectional‐50 dependence of multi‐variate binary data. The resulting class of generalized binary vector auto‐regressive (gbVAR) models is parsimonious, nicely interpretable and allows also to model negative dependence. We provide stationarity conditions and derive moving‐average‐type representations that allow to prove geometric mixing properties. Furthermore, we derive general stochastic properties of gbVAR processes, including formulae for transition probabilities. In particular, classical Yule–Walker equations hold that facilitate parameter estimation in gbVAR models. In simulations, we investigate the estimation performance, and for illustration, we apply gbVAR models to particulate matter (PM10, ‘fine dust’) alarm data observed at six monitoring stations in Stuttgart, Germany.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of Time Series Analysis ; ISSN: 1467-9892 ; Volume: 43 ; Year: 2021 ; Issue: 2 ; Pages: 285-311 ; Oxford, UK: John Wiley & Sons, Ltd

Classification and Topics

• Subject: Binary data ; mixing properties ; multi‐variate time series ; stationarity conditions ; transition probabilities ; Yule–Walker equations

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Jentsch, Carsten ; Reichmann, Lena
• Event: Veröffentlichung
• (who): John Wiley & Sons, Ltd
• (where): Oxford, UK
• (when): 2021

Further information

• DOI: doi:10.1111/jtsa.12614
• Handle: http://hdl.handle.net/10419/284809
• Last update: 10.03.2025, 11:45 AM CET