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

被引：1

作者：

Kooths, S ^{[1
]}

Mitze, T ^{[1
]}

Ringhut, E ^{[1
]}

机构：

[1] Muenster Inst Computat Econ, D-48149 Munster, Germany

来源：

2003 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR FINANCIAL ENGINEERING, PROCEEDINGS | 2003年

关键词：

inflation forecasting; artificial intelligence; fuzzy rule-bases;

D O I：

10.1109/CIFER.2003.1196259

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

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.

引用

页码：183 / 190

页数：8

共 16 条

[1]

ALTIMARI SN, 2001, 63 ECB

[2]

[Anonymous], SSE EFI WORKING PAPE

[3] An I(2) analysis of inflation and the markup [J].

Banerjee, A ;

Cockerell, L ;

Russell, B .

JOURNAL OF APPLIED ECONOMETRICS, 2001, 16 (03) :221-240

[4]

BERLEMANN M, 2002, FORECASTING INFLATIO

[5] EFFICIENT INFERENCE ON COINTEGRATION PARAMETERS IN STRUCTURAL ERROR-CORRECTION MODELS [J].

BOSWIJK, HP .

JOURNAL OF ECONOMETRICS, 1995, 69 (01) :133-158

[6] Modeling inflation in Australia [J].

de Brouwer, G ;

Ericsson, NR .

JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 1998, 16 (04) :433-449

[7]

Fagan Gabriel., 2001, 42 ECB

[8]

GERLACH S, 2001, 98 BIS

[9]

GORDON O, 1997, INT J APPROX REASON, V17, P369

[10]

GRANGER C, 2001, UNPUB THICK MODELLIN

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