Exact maximum-likelihood estimation of autoregressive models via the Kalman filter

DOI10.1016/0165-1765(86)90231-4zbMath1328.62542OpenAlexW2058075133MaRDI QIDQ899876

Francis X. Diebold

Publication date: 1 January 2016

Published in: Economics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0165-1765(86)90231-4

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items

Filtering nonlinear spatio-temporal chaos with autoregressive linear stochastic models ⋮ State space modeling of time series: A review essay ⋮ ARCH modeling in finance. A review of the theory and empirical evidence ⋮ Recursive estimation in econometrics ⋮ Derivation of the unconditional state-covariance matrix for exact maximum-likelihood estimation of ARMA models

Uses Software

• AS 154

Cites Work

• The exact initial covariance matrix of the state vector of a general \(MA(q)\) process
• Algorithm AS 154: An Algorithm for Exact Maximum Likelihood Estimation of Autoregressive-Moving Average Models by Means of Kalman Filtering
• Evaluation of likelihood functions for Gaussian signals
• Unnamed Item
• Unnamed Item