An automatic leading indicator, variable reduction and variable selection methods using small and large datasets: Forecasting the industrial production growth for euro area economies

Gonzalo Camba-Mendez, George Kapetanios, Fotis Papailias and Martin Weale ()

No 1773, Working Paper Series from European Central Bank

Abstract: This paper assesses the forecasting performance of various variable reduction and variable selection methods. A small and a large set of wisely chosen variables are used in forecasting the industrial production growth for four Euro Area economies. The results indicate that the Automatic Leading Indicator (ALI) model performs well compared to other variable reduction methods in small datasets. However, Partial Least Squares and variable selection using heuristic optimisations of information criteria along with the ALI could be used in model averaging methodologies. JEL Classification: C11, C32, C52

Keywords: Bayesian shrinkage regression; dynamic factor model; euro area; forecasting; Kalman filter; partial least squares (search for similar items in EconPapers)
Date: 2015-04
New Economics Papers: this item is included in nep-ecm, nep-eec and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.ecb.europa.eu//pub/pdf/scpwps/ecbwp1773.en.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20151773

Access Statistics for this paper

More papers in Working Paper Series from European Central Bank 60640 Frankfurt am Main, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Official Publications ().