IDEAS home Printed from https://ideas.repec.org/p/ags/monebs/267908.html
   My bibliography  Save this paper

Estimation of Regression Disturbances Based on Minimum Message Length

Author

Listed:
  • Laskar, Mizan R.
  • King, Maxwell L.

Abstract

This paper derives six different forms of message length functions for the general linear regression model. In so doing, two different prior densities and the idea of parameter orthogonality are employed. Parameter estimates are then obtained by finding those parameter values which minimize the message length. The asymptotic properties of the minimum message length (MML) estimators are studied and it is shown that these estimators are asymptotically normal. A Monte Carlo experiment was conducted to investigate the small sample properties of the MML estimators in the context of first-order moving average regression disturbances. The results show that the combination of parameter orthogonality and message length based inference can produce good small sample properties.

Suggested Citation

Handle: RePEc:ags:monebs:267908
DOI: 10.22004/ag.econ.267908
as

Download full text from publisher

File URL: https://ageconsearch.umn.edu/record/267908/files/monash-215.pdf
Download Restriction: no
File URL: https://ageconsearch.umn.edu/record/267908/files/monash-215.pdf?subformat=pdfa
Download Restriction: no
File URL: https://libkey.io/10.22004/ag.econ.267908?utm_source=ideas
LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
---><---

More about this item

Keywords

;

Statistics

Access and download statistics

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:monebs:267908. See general information about how to correct material in RePEc.

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

We have no bibliographic references for this item. You can help adding them by using this form .

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: .

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.