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Flexible Tool for Model Building: the Relevant Transformation of the Inputs Network Approach (RETINA)

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Author Info

  • TEODOSIO PEREZ-AMARAL

    ()
    (Departamento de Analisis Economico, Universidad Complutense de Madrid.)

  • GIAMPIERO M. GALLO

    ()
    (Dipartimento di Statistica “G. Parenti”, Università di Firenze, Viale G.B.Morgagni,)

  • HALBERT WHITE

    ()
    (Department of Economics, University of California, San Diego, 9500 Gilman Drive. La Jolla, CA, 92093-0508, USA)

Abstract

A new method, called Relevant Transformation of the Inputs Network Approach (RETINA) isproposed as a tool for model building. It is designed around flexibility (with nonlinear transformations of the predictors of interest), selective search within the range of possible models, out-of-sample forecasting ability and computational simplicity. In tests on simulated data, it shows both a high rate of successful retrieval of the DGP which increases with the sample size and a good performance relative to other alternative procedures. A telephone service demand model is built to show how the procedure applies on real data.

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File URL: http://eprints.ucm.es/7689/1/0309.pdf
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Bibliographic Info

Paper provided by Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales in its series Documentos del Instituto Complutense de Análisis Económico with number 0309.

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Length: 23 pages
Date of creation: 2003
Date of revision:
Handle: RePEc:ucm:doicae:0309

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Related research

Keywords: Relevant Transformation of the Inputs Network Approach (RETINA); Economics models;

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References

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  1. Granger, Clive W. J. & King, Maxwell L. & White, Halbert, 1995. "Comments on testing economic theories and the use of model selection criteria," Journal of Econometrics, Elsevier, vol. 67(1), pages 173-187, May.
  2. Krolzig, Hans-Martin & Hendry, David F., 2001. "Computer automation of general-to-specific model selection procedures," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 831-866, June.
  3. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  4. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  5. Clive Granger & Allan Timmermann, 1999. "Data mining with local model specification uncertainty: a discussion of Hoover and Perez," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 220-225.
  6. Kevin D. Hoover & Stephen J. Perez, 1999. "Data mining reconsidered: encompassing and the general-to-specific approach to specification search," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
  7. Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, EconWPA.
  8. Kenneth D. West, 1994. "Asymptotic Inference About Predictive Ability," Macroeconomics 9410002, EconWPA.
  9. Sin, Chor-Yiu & White, Halbert, 1996. "Information criteria for selecting possibly misspecified parametric models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 207-225.
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Citations

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Cited by:
  1. Doppelhofer, G. & Weeks, M., 2005. "Jointness of Growth Determinants," Cambridge Working Papers in Economics 0542, Faculty of Economics, University of Cambridge.
  2. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
  3. Kock, Anders Bredahl & Teräsvirta, Timo, 2014. "Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009," International Journal of Forecasting, Elsevier, vol. 30(3), pages 616-631.
  4. Ivan Savin & Peter Winker, 2012. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection: Application on the Russian Innovative Performance," Computational Economics, Society for Computational Economics, vol. 39(4), pages 337-363, April.
  5. Eduardo Acosta-González & Fernando Fernández-Rodríguez, 2014. "Forecasting Financial Failure of Firms via Genetic Algorithms," Computational Economics, Society for Computational Economics, vol. 43(2), pages 133-157, February.
  6. Gernot Doppelhofer & Xavier Sala I Martin & Melvyn Weeks, 2005. "Jointness of Determinants of Economics Growth," Money Macro and Finance (MMF) Research Group Conference 2005 54, Money Macro and Finance Research Group.
  7. David Hendry & Hans-Martin Krolzig, 2004. "We Ran One Regression," Economics Series Working Papers 2004-W17, University of Oxford, Department of Economics.
  8. Ivan Savin, 2010. "A comparative study of the Lasso-type and heuristic model selection methods," Working Papers 042, COMISEF.
  9. Camila Epprecht & Dominique Guegan & Álvaro Veiga, 2013. "Comparing variable selection techniques for linear regression: LASSO and Autometrics," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00917797, HAL.
  10. Massimiliano Marinucci & Teodosio Pérez-Amaral, 2005. "Econometric Modeling of Business Telecommunications Demand using RETINA and Finite Mixtures," Econometrics 0505003, EconWPA, revised 14 May 2005.
  11. Andreas Sachs & Frauke Schleer, 2013. "Labour market performance in OECD countries: A comprehensive empirical modelling approach of institutional interdependencies," WWWforEurope Working Papers series 7, WWWforEurope.
  12. Marcin Blazejowski & Pawel Kufel & Tadeusz Kufel, 2009. "Automatic Procedure of Building Congruent Dynamic Model in Gretl," EHUCHAPS, Universidad del País Vasco - Facultad de Ciencias Económicas y Empresariales.

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