Model Selection Criteria Using Likelihood Functions And Out-Of-Sample Performance
Model selection is often conducted by ranking models by their out-of-sample forecast error. Such criteria only incorporate information about the expected value, whereas models usually describe the entire probability distribution. Hence, researchers may desire a criteria evaluating the performance of the entire probability distribution. Such a method is proposed and is found to increase the likelihood of selecting the true model relative to conventional model ranking techniques.
|Date of creation:||2001|
|Contact details of provider:|| Web page: http://www.agebb.missouri.edu/ncrext/ncr134/|
When requesting a correction, please mention this item's handle: RePEc:ags:ncrone:18947. 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 references are entirely missing, you can add them using this form.