Variable Selection using Non-Standard Optimisation of Information Criteria
The question of variable selection in a regression model is a major open research topic in econometrics. Traditionally two broad classes of methods have been used. One is sequential testing and the other is information criteria. The advent of large datasets used by institutions such as central banks has exacerbated this model selection problem. This paper provides a new solution in the context of information criteria. The solution rests on the judicious selection of a subset of models for consideration using nonstandard optimisation algorithms for information criterion minimisation. In particular, simulated annealing and genetic algorithms are considered. Both a Monte Carlo study and an empirical forecasting application to UK CPI infation suggest that the new methods are worthy of further consideration.
|Date of creation:||May 2005|
|Contact details of provider:|| Postal: London E1 4NS|
Phone: +44 (0) 20 7882 5096
Fax: +44 (0) 20 8983 3580
Web page: http://www.econ.qmul.ac.uk
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:qmw:qmwecw:wp533. 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: (Nick Vriend)
If references are entirely missing, you can add them using this form.