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Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods

Research output: Contribution to journalArticle

George Kapetanios, Massimiliano Marcellino, Fotis Papailias

Original languageEnglish
Pages (from-to)369-382
JournalCOMPUTATIONAL STATISTICS AND DATA ANALYSIS
Volume100
Early online date18 Mar 2015
DOIs
Accepted/In press27 Feb 2015
E-pub ahead of print18 Mar 2015
Published1 Aug 2016

King's Authors

Abstract

Forecasting macroeconomic variables using many predictors is considered. Model selection and model reduction approaches are compared. Model selection includes heuristic optimisation of information criteria using: simulated annealing, genetic algorithms, MC3 and sequential testing. Model reduction employs the methods of principal components, partial least squares and Bayesian shrinkage regression. The problem of unbalanced datasets is discussed and potential solutions are suggested. An out-of-sample forecasting exercise provides evidence that these methods are useful in predicting the growth rates of quarterly GDP and monthly inflation.

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