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

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• 作者：George Kapetanios^a ; Massimiliano Marcellino^b ; ^c ; ^d ; Fotis Papailias^e ; ^f ; ^{f.papailias@qub.ac.uk" class="auth_mail" title="E-mail the corresponding author} ; ^{f.papailias@quantf.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Heuristic optimisation ; Information criteria ; Unbalanced datasets ; Forecasting ; Inflation ; GDP ; Principal components ; Partial least squares ; Bayesian shrinkage regression
• 刊名：Computational Statistics & Data Analysis
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：100
• 期：Complete
• 页码：369-382
• 全文大小：598 K

文摘

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.