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

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  • Teodosio Perez-Amaral
  • Giampiero M. Gallo
  • Halbert White

Abstract

A new method, called Relevant Transformation of the Inputs Network Approach is proposed 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 data generating process, 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. Copyright 2003 Blackwell Publishing Ltd.

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Bibliographic Info

Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics & Statistics.

Volume (Year): 65 (2003)
Issue (Month): s1 (December)
Pages: 821-838

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Handle: RePEc:bla:obuest:v:65:y:2003:i:s1:p:821-838

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References

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  1. 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.
  2. 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.
  3. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  4. Hans-Martin Krolzig & David Hendry, 1999. "Computer Automation of General-to-Specific Model Selection Procedures," Computing in Economics and Finance 1999 314, Society for Computational Economics.
  5. Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, EconWPA.
  6. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-84, September.
  7. 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.
  8. 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.
  9. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
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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. Ivan Savin, 2010. "A comparative study of the Lasso-type and heuristic model selection methods," Working Papers 042, COMISEF.
  3. Sachs, Andreas & Schleer, Frauke, 2013. "Labour market performance in OECD countries: A comprehensive empirical modelling approach of institutional interdependencies," ZEW Discussion Papers 13-040, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  4. 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.
  5. Massimiliano Marinucci & Teodosio Pérez-Amaral, 2005. "Econometric modeling of business Telecommunications demand using Retina and Finite Mixtues," Documentos de Trabajo del ICAE 0501, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
  6. 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.
  7. Camila Epprecht & Dominique Guegan & Álvaro Veiga, 2013. "Comparing variable selection techniques for linear regression: LASSO and Autometrics," Documents de travail du Centre d'Economie de la Sorbonne 13080, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  8. David F. Hendry & Hans-Martin Krolzig, 2004. "We Ran One Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 799-810, December.
  9. 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.
  10. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
  11. 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.
  12. 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.
  13. 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.

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