Information Theoretic Estimators of the First-Order Spatial Autoregressive Model
Information theoretic estimators for the first-order autoregressive model are considered. Extensive Monte Carlo experiments are used to compare finite sample performance of traditional and three information theoretic estimators including maximum empirical likelihood, maximum empirical exponential likelihood, and maximum log Euclidean likelihood. It is found that information theoretic estimators are robust to specification of spatial autocorrelation and dominate traditional estimators in finite samples. Finally, the proposed estimators are applied to an illustrative example of hedonic housing pricing.
|Date of creation:||2009|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: (414) 918-3190
Fax: (414) 276-3349
Web page: http://www.aaea.org
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:ags:aaea09:49491. 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: (AgEcon Search)
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.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.