Nonlinear Forecasting of Euro Area Industrial Production Using Evolutionary Approaches

Christos Avdoulas () and Stelios Bekiros
Additional contact information
Christos Avdoulas: Athens University of Economics and Business (AUEB)

Computational Economics, 2018, vol. 52, issue 2, No 10, 530 pages

Abstract: Abstract Stock Watson (in: Mills T, Patterson K (eds) Palgrave handbook of econometrics, Palgrave MacMillan, Basingstoke, 2003) argue that robust forecastability is dependent upon the optimality of the estimated parameters. Whilst recent studies in macroeconomic forecasting report the superiority of nonlinear models, yet they still suffer from precise parameter estimation. Our approach introduces evolutionary programming to optimize the parameters of various Threshold Autoregressive models. We generate forecasts for industrial production and compare our results versus linear benchmarks and quasi-maximum likelihood estimates for three Euro area countries. Based on our robust method, central banks and policy-makers could dynamically adjust their monetary and fiscal policy predictions.

Keywords: Growth forecasting; Nonlinear models; Evolutionary methods (search for similar items in EconPapers)
JEL-codes: C32 C58 E2 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-017-9695-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:kap:compec:v:52:y:2018:i:2:d:10.1007_s10614-017-9695-3

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-017-9695-3

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().