A Monte Carlo Analysis of Alternative Estimators in Models Involving Selectivity
In a simultaneous-equation model involving selectivity, Monte Carlo and response-surface techniques are used to assess the performance of five estimators commonly applied to a behavioral equation conditioned on an endogenous binary selectivity decision. The estimators include least squares with an exogenous dummy variable for the selectivity decision, three two-stage estimators that employ the estimated probability of the selectivity decision, and full information maximum likelihood (FIML). Although formally inconsistent, least squares with dummy variables is found to perform nearly as well as FIML, based on mean squared error measures. All two-stage estimators are found to be seriously deficient in terms of robustness.
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.
Volume (Year): 9 (1991)
Issue (Month): 1 (January)
|Contact details of provider:|| Web page: http://www.amstat.org/publications/jbes/index.cfm?fuseaction=main |
|Order Information:||Web: http://www.amstat.org/publications/index.html|
When requesting a correction, please mention this item's handle: RePEc:bes:jnlbes:v:9:y:1991:i:1:p:41-49. 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: (Christopher F. Baum)
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.