Arbeitspapier
A simple variable selection technique for nonlinear models
Applying nonparametric variable selection criteria in nonlinear regression models generally requires a substantial computational effort if the data set is large. In this paper we present a selection technique that is computationally much less demanding and performs well in comparison with methods currently available. It is based on a Taylor expansion of the nonlinear model around a given point in the sample space. Performing the selection only requires repeated least squares estimation of models that are linear in parameters. The main limitation of the method is that the number of variables among which to select cannot be very large if the sample is small and an adequate Taylor expansion is of high order. Large samples can be handled without problems.
- Language
-
Englisch
- Bibliographic citation
-
Series: SFB 373 Discussion Paper ; No. 1999,26
- Classification
-
Wirtschaft
- Subject
-
nonlinear regression
Autoregression
nonlinear time series
nonparametric variable selection
time series modelling
- Event
-
Geistige Schöpfung
- (who)
-
Rech, Gianluigi
Teräsvirta, Timo
Tschernig, Rolf
- Event
-
Veröffentlichung
- (who)
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Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
- (where)
-
Berlin
- (when)
-
1999
- Handle
- URN
-
urn:nbn:de:kobv:11-10056169
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Rech, Gianluigi
- Teräsvirta, Timo
- Tschernig, Rolf
- Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
Time of origin
- 1999
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