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

George Kapetanios, Massimiliano Marcellino and Fotis Papailias

Computational Statistics & Data Analysis, 2016, vol. 100, issue C, 369-382

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.

Keywords: Heuristic optimisation; Information criteria; Unbalanced datasets; Forecasting; Inflation; GDP; Principal components; Partial least squares; Bayesian shrinkage regression (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:100:y:2016:i:c:p:369-382

DOI: 10.1016/j.csda.2015.02.017

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