Prediction Intervals for Arima Models
The problem of constructing prediction intervals for linear time series (ARIMA) models is examined. The aim is to find prediction intervals which incorporate an allowance for sampling error associated with parameter estimates. The effect of constraints on parameters arising from stationary and invertibility conditions is also incorporated. Two new methods, based to varying degrees on first-order Taylor approximations, are proposed.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||1997|
|Contact details of provider:|| Postal: PO Box 11E, Monash University, Victoria 3800, Australia|
Phone: +61 3 99052489
Fax: +61 3 99055474
Web page: http://business.monash.edu/econometrics-and-business-statistics
More information through EDIRC
|Order Information:|| Web: http://business.monash.edu/econometrics-and-business-statistics Email: |
When requesting a correction, please mention this item's handle: RePEc:msh:ebswps:1997-8. 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: (Dr Xibin Zhang)
If references are entirely missing, you can add them using this form.