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.