A note on reparameterizing a vector autoregressive moving average model to enforce stationarity

DOI10.1080/00949658608810893zbMath0604.62088OpenAlexW2004839075MaRDI QIDQ3740862

Robert Kohn, Craig F. Ansley

Publication date: 1986

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949658608810893

zbMATH Keywords

Cholesky factorizationstationarityparameterizationfast computationautocovariancesvector autoregressive moving average modelvector ARMA model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (2)

Collective Anomaly Detection in High-Dimensional Var Models ⋮ Detecting granular time series in large panels

Cites Work

• Covariance characterization by partial autocorrelation matrices
• On the parametrization of autoregressive models by partial autocorrelations
• Some efficient computational procedures for high order ARMA models
• The effect of transformations of variables upon their correlation coefficients
• Maximum Likelihood Fitting of ARMA Models to Time Series with Missing Observations
• Exact likelihood of vector autoregressive-moving average process with missing or aggregated data
• A note on obtaining the theoretical autocovariances of an ARMA process
• Some new algorithms for recursive estimation in constant, linear, discrete-time systems

This page was built for publication: A note on reparameterizing a vector autoregressive moving average model to enforce stationarity