Forecasting with VARMA Models
Vector autoregressive moving-average (VARMA) processes are suitable models for producing linear forecasts of sets of time series variables. They provide parsimonious representations of linear data generation processes (DGPs). The setup for these processes in the presence of cointegrated variables is considered. Moreover, a unique or identified parameterization based on the echelon form is presented. Model specification, estimation, model checking and forecasting are discussed. Special attention is paid to forecasting issues related to contemporaneously and temporally aggregated processes.
|Date of creation:||2004|
|Contact details of provider:|| Postal: Badia Fiesolana, Via dei Roccettini, 9, 50014 San Domenico di Fiesole (FI) Italy|
Web page: http://www.eui.eu/ECO/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:eui:euiwps:eco2004/25. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Anne Banks)
If references are entirely missing, you can add them using this form.