Maximum likelihood estimation for linear regression models with autoregressive errors

DOI10.1080/02331889108802302zbMath0809.62083OpenAlexW2084560353MaRDI QIDQ4322947

Stefan Schäffler

Publication date: 13 February 1995

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331889108802302

zbMATH Keywords

existenceuniquenessnonlinear optimizationautomatic differentiationstepsize controlstationary \(AR(p)\) errors

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05) Point estimation (62F10)

Related Items (5)

ESTIMATING PARAMETERS IN AUTOREGRESSIVE MODELS IN NON-NORMAL SITUATIONS: ASYMMETRIC INNOVATIONS ⋮ Estimating parameters of a multiple autoregressive model by the modified maximum likelihood method ⋮ Estimating parameters in autoregressive models with asymmetric innovations ⋮ Autoregressive models with short-tailed symmetric distributions ⋮ Estimating parameters in autoregressive models in non-normal situations: symmetric innovations

Cites Work

• Strong consistency of least squares estimates in dynamic models
• When is an altoregressive scheme stationary
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Maximum likelihood estimation for linear regression models with autoregressive errors