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Autor:innen: 
Erscheinungsjahr: 
2005
Schriftenreihe/Nr.: 
Working Paper No. 533
Verlag: 
Queen Mary University of London, Department of Economics, London
Zusammenfassung: 
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.
Schlagwörter: 
Simulated Annealing, Genetic Algorithms, Information criteria, Model selection, Forecasting, Inflation
JEL: 
C11
C15
C53
Dokumentart: 
Working Paper

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