A Comparison of Complementary Automatic Modeling Methods: RETINA and PcGets
In Perez-Amaral, Gallo, and White (2003), the authors proposed an automatic predictive modelling 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 US 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 modelling tool chest.
|Date of creation:||04 Oct 2004|
|Date of revision:|
|Contact details of provider:|| Postal: Viale G.B. Morgagni, 59 - I-50134 Firenze - Italy|
Phone: +39 055 2751500
Fax: +39 055 4223560
Web page: http://www.disia.unifi.it/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:fir:econom:wp2004_12. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Francesco Calvori)
If references are entirely missing, you can add them using this form.