Variable Selection using Non-Standard Optimisation of Information Criteria

George Kapetanios

No 533, Working Papers from Queen Mary University of London, School of Economics and Finance

Abstract: 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.

Keywords: Simulated Annealing; Genetic Algorithms; Information criteria; Model selection; Forecasting; Inflation (search for similar items in EconPapers)
JEL-codes: C11 C15 C53 (search for similar items in EconPapers)
Date: 2005-05-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.qmul.ac.uk/sef/media/econ/research/wor ... 2005/items/wp533.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:qmw:qmwecw:533

Access Statistics for this paper

More papers in Working Papers from Queen Mary University of London, School of Economics and Finance Contact information at EDIRC.
Bibliographic data for series maintained by Nicholas Owen (n.j.owen@qmul.ac.uk this e-mail address is bad, please contact repec@repec.org).