Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-27T18:50:48.112Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

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.

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Campos, J., N.R. Ericsson, & D.F. Hendry (2004) General-to-Specific Modeling. Edward Elgar.
Hendry, D.F. & H.-M. Krolzig (2001) Automatic Econometric Model Selection with PcGets. Timberlake Consultants Press.
Hendry, D.F. & H.-M. Krolzig (2003) New developments in automatic general-to-specific modeling. In B.P. Stigum (ed.), Econometrics and the Philosophy of Economics, pp. 379–419. Princeton University Press.
Hendry, D.F. & H.-M. Krolzig (2004) Sub-sample model selection procedures in general-to-specific modelling. In R. Becker & S. Hurn (eds.), Contemporary Issues in Economics and Econometrics: Theory and Applications, pp. 53–75. Edward Elgar.
Hoover, K. & S. Perez (1999) Data mining reconsidered: Encompassing and the general-to-specific approach to specification search. Econometrics Journal 2, 167–191.Google Scholar
Krolzig, H.-M. & D.F. Hendry (2001) Computer automation of general-to-specific model selection procedures. Journal of Economic Dynamics and Control 25, 831–866.Google Scholar
Perez-Amaral, T., G.M. Gallo, & H. White (2003) A flexible tool for model building: The Relevant Transformation of the Inputs Network Approach (RETINA). Oxford Bulletin of Economics and Statistics 65, supplement 1, 821–838.Google Scholar
Ploberger, W. & P.C.B. Phillips (2003) Empirical limits for time series econometric models. Econometrica 71, 627–673.Google Scholar
White, H. (1998) Artificial Neural Network and Alternative Methods for Assessing Naval Readiness. Technical Report, NeuralNet R&D Associates, San Diego.
White, H. (1989) Learning in artificial neural networks: A statistical perspective. Neural Computation 1, 425–464. Reprinted in H. White (1992) Artificial Neural Networks: Approximation and Learning Theory pp. 90–131. Blackwell.Google Scholar