The Properties of Automatic Gets Modelling
We examine the properties of automatic model selection, as embodied in PcGets, and evaluate its performance across different (unknown) states of nature. After describing the basic algorithm and some recent changes, we discuss the consistency of its selection procedures, then examine the extent to which model selection is non-distortionary at relevant sample sizes. The problems posed in judging performance on collinear data are noted. The conclusion notes how PcGets can handle more variables than observations, and hence how it can tackle non-linear models.
|Date of creation:||01 Mar 2003|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.economics.ox.ac.uk/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
When requesting a correction, please mention this item's handle: RePEc:oxf:wpaper:2003-w14. 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: (Caroline Wise)The email address of this maintainer does not seem to be valid anymore. Please ask Caroline Wise to update the entry or send us the correct address
If references are entirely missing, you can add them using this form.