Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/61750 
Year of Publication: 
1999
Series/Report no.: 
SFB 373 Discussion Paper No. 1999,26
Publisher: 
Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes, Berlin
Abstract: 
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.
Subjects: 
nonlinear regression
Autoregression
nonlinear time series
nonparametric variable selection
time series modelling
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
232.94 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.