ESTIMATION OF MULTIVARIATE TIME SERIES

DOI10.1111/j.1467-9892.1987.tb00423.xzbMath0613.62116OpenAlexW2091146261MaRDI QIDQ4720615

Brian L. Shea

Publication date: 1987

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9892.1987.tb00423.x

zbMATH Keywords

algorithmKalman filtermultivariate time seriesautoregressive moving average modelexact likelihoodvector ARMA modelinverse cross covariances

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Numerical approximation and computational geometry (primarily algorithms) (65D99)

Related Items (8)

Goodness-of-fit tests for Laplace, Gaussian and exponential power distributions based on λ-th power skewness and kurtosis ⋮ Estimating multivariate autoregressive moving average models by fitting long autoregressions ⋮ An effectiveness study of the Bayesian inference with multivariate autoregressive moving average processes ⋮ Computing optimal adjustment schemes for the general tool-wear problem ⋮ Exact maximum likelihood estimation of structured or unit root multivariate time series models ⋮ Goodness‐of‐fit tests of normality for the innovations in ARMA models ⋮ On time-irreversibility and other non-linear features in time series ⋮ Derivation of the unconditional state-covariance matrix for exact maximum-likelihood estimation of ARMA models

Uses Software

• NAG

Cites Work

• Unnamed Item
• Maximum Likelihood Fitting of ARMA Models to Time Series with Missing Observations
• Algorithm AS 154: An Algorithm for Exact Maximum Likelihood Estimation of Autoregressive-Moving Average Models by Means of Kalman Filtering
• Modeling Multiple Times Series with Applications
• Exact likelihood of vector autoregressive-moving average process with missing or aggregated data
• A note on obtaining the theoretical autocovariances of an ARMA process
• On the fitting of multivariate autoregressions, and the approximate canonical factorization of a spectral density matrix

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