Date: 2018
Type: Article
Nonlinear forecasting of euro area industrial production using evolutionary approaches
Computational economics, 2018, Vol. 52, No. 2, pp. 521–530
AVDOULAS, Christos, BEKIROS, Stelios D., Nonlinear forecasting of euro area industrial production using evolutionary approaches, Computational economics, 2018, Vol. 52, No. 2, pp. 521–530
- https://hdl.handle.net/1814/49792
Retrieved from Cadmus, EUI Research Repository
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.
Additional information:
Published online: 22 May 2017
Cadmus permanent link: https://hdl.handle.net/1814/49792
Full-text via DOI: 10.1007/s10614-017-9695-3
ISSN: 0927-7099; 1572-9974
Publisher: Springer
Keyword(s): Growth forecasting Nonlinear models Evolutionary methods C32 C58 E2
Files associated with this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |