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A COMPARISON OF COMPLEMENTARY AUTOMATIC MODELING METHODS: RETINA AND PcGets

Published online by Cambridge University Press:  08 February 2005

Teodosio Perez-Amaral ,
Giampiero M. Gallo  and
Halbert White
Teodosio Perez-Amaral
Affiliation:
Universidad Complutense de Madrid
Giampiero M. Gallo
Affiliation:
Università de Firenze
Halbert White
Affiliation:
University of California at San Diego
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Abstract

In Perez-Amaral, Gallo, and White (2003, Oxford Bulletin of Economics and Statstics 65, 821–838), the authors proposed an automatic predictive modeling tool called relevant transformation of the inputs network approach (RETINA). It is designed to embody flexibility (using nonlinear transformations of the predictors of interest), selective search within the range of possible models, control of collinearity, out-of-sample forecasting ability, and computational simplicity. In this paper we compare the characteristics of RETINA with PcGets, a well-known automatic modeling method proposed by David Hendry. We point out similarities, differences, and complementarities of the two methods. In an example using U.S. telecommunications demand data we find that RETINA can improve both in- and out-of-sample over the usual linear regression model and over some models suggested by PcGets. Thus, both methods are useful components of the modern applied econometrician's automated modeling tool chest.

Information

Type
Research Article
Information
Econometric Theory , Volume 21 , Issue 1 , February 2005 , pp. 262 - 277
Copyright
© 2005 Cambridge University Press

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References

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