Inflation forecasting

A comparison between econometric methods and a computational approach based on genetic-neural fuzzy rule-bases

S. Kooths, T. Mitze, E. Ringhut

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Resumé

The paper seeks to determine whether the predictive power of linear econometric models outperforms models based on artificial intelligence methods (computational methods) concerning forecasting inflation. Various models of both types are constructed and compared according to a battery of test statistics. We find some superiority of the computational approach.

OriginalsprogEngelsk
Titel2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings
Antal sider8
Vol/bind2003-January
ForlagIEEE
Publikationsdato1. jan. 2003
Sider183-190
Artikelnummer1196259
ISBN (Elektronisk)0780376544
DOI
StatusUdgivet - 1. jan. 2003
Begivenhed2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Hong Kong, Kina
Varighed: 20. mar. 200323. mar. 2003

Konference

Konference2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003
LandKina
ByHong Kong
Periode20/03/200323/03/2003
SponsorThe Institute of Electrical and Electronics Engineers Neural Networks Society (NNS)

Fingeraftryk

Inflation forecasting
Econometric methods
Artificial intelligence
Test statistic
Predictive power
Computational methods
Econometric models

Citer dette

Kooths, S., Mitze, T., & Ringhut, E. (2003). Inflation forecasting: A comparison between econometric methods and a computational approach based on genetic-neural fuzzy rule-bases. I 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings (Bind 2003-January, s. 183-190). [1196259] IEEE. https://doi.org/10.1109/CIFER.2003.1196259
Kooths, S. ; Mitze, T. ; Ringhut, E. / Inflation forecasting : A comparison between econometric methods and a computational approach based on genetic-neural fuzzy rule-bases. 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings. Bind 2003-January IEEE, 2003. s. 183-190
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keywords = "Artificial neural networks, Computational intelligence, Econometrics, Economic forecasting, Economic indicators, Exchange rates, Industrial economics, Instruments, Power generation economics, Predictive models",
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Kooths, S, Mitze, T & Ringhut, E 2003, Inflation forecasting: A comparison between econometric methods and a computational approach based on genetic-neural fuzzy rule-bases. i 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings. bind 2003-January, 1196259, IEEE, s. 183-190, 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003, Hong Kong, Kina, 20/03/2003. https://doi.org/10.1109/CIFER.2003.1196259

Inflation forecasting : A comparison between econometric methods and a computational approach based on genetic-neural fuzzy rule-bases. / Kooths, S.; Mitze, T.; Ringhut, E.

2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings. Bind 2003-January IEEE, 2003. s. 183-190 1196259.

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

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KW - Economic indicators

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KW - Power generation economics

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Kooths S, Mitze T, Ringhut E. Inflation forecasting: A comparison between econometric methods and a computational approach based on genetic-neural fuzzy rule-bases. I 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings. Bind 2003-January. IEEE. 2003. s. 183-190. 1196259 https://doi.org/10.1109/CIFER.2003.1196259