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• W2069897430 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. A solution in the context of information criteria is provided in this paper. 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 inflation suggest that the proposed methods are worthy of further consideration." @default.
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• W2069897430 date "2007-09-01" @default.
• W2069897430 modified "2023-10-16" @default.
• W2069897430 title "Variable selection in regression models using nonstandard optimisation of information criteria" @default.
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• W2069897430 doi "https://doi.org/10.1016/j.csda.2007.04.006" @default.
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