Computing and using residuals in time series models

DOI10.1016/j.csda.2007.05.034zbMath1452.62664OpenAlexW2032347311MaRDI QIDQ1023503

José Alberto Mauricio

Publication date: 12 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2007.05.034

zbMATH Keywords

innovationsautoregressive moving average modelconditional residualsnormalized residualsunconditional residuals

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items

Beta seasonal autoregressive moving average models

Uses Software

• itsmr
• AS 154
• AS 197

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Exact maximum likelihood estimation of partially nonstationary vector ARMA models
• Finite sample properties of estimators for autoregressive moving average models
• Algorithm AS 197: A Fast Algorithm for the Exact Likelihood of Autoregressive-Moving Average Models
• Algorithm AS 154: An Algorithm for Exact Maximum Likelihood Estimation of Autoregressive-Moving Average Models by Means of Kalman Filtering
• On a measure of lack of fit in time series models
• The likelihood function of stationary autoregressive-moving average models
• An algorithm for the exact likelihood of a mixed autoregressive-moving average process
• Introduction to Time Series and Forecasting
• Exact Maximum Likelihood Estimation of Stationary Vector ARMA Models
• An algorithm for the exact likelihood of a stationary vector autoregressive‐moving average model
• Matrix Analysis of Linear Time-Varying Circuits
• Elements of multivariate time series analysis.