A COMPARISON OF COMPLEMENTARY AUTOMATIC MODELING METHODS: RETINA AND PcGets
AbstractIn 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.
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Bibliographic InfoArticle provided by Cambridge University Press in its journal Econometric Theory.
Volume (Year): 21 (2005)
Issue (Month): 01 (February)
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Other versions of this item:
- Teodosio Perez-Amaral & Giampiero M. Gallo & Halbert L. White, 2004. "A Comparison of Complementary Automatic Modeling Methods: RETINA and PcGets," Econometrics Working Papers Archive wp2004_12, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
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