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The Properties of Automatic Gets Modelling

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  • David Hendry
  • Hans-Martin Krolzig

Abstract

We examine the properties of automatic model selection, as embodied in PcGets, and evaluate its performance across different (unknown) states of nature. After describing the basic algorithm and some recent changes, we discuss the consistency of its selection procedures, then examine the extent to which model selection is non-distortionary at relevant sample sizes. The problems posed in judging performance on collinear data are noted. The conclusion notes how PcGets can handle more variables than observations, and hence how it can tackle non-linear models.

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Bibliographic Info

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 2003-W14.

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Date of creation: 01 Mar 2003
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Handle: RePEc:oxf:wpaper:2003-w14

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  1. 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, Royal Economic Society, vol. 2(2), pages 167-191.
  2. Bruce E. Hansen, 1999. "Discussion of 'Data mining reconsidered'," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 2(2), pages 192-201.
  3. Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Econometric Society World Congress 2000 Contributed Papers, Econometric Society 0411, Econometric Society.
  4. P. Dorian Owen, 2003. "General-to-Specific Modelling Using PcGets," Journal of Economic Surveys, Wiley Blackwell, Wiley Blackwell, vol. 17(4), pages 609-628, 09.
  5. Breusch, T S & Pagan, A R, 1980. "The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 47(1), pages 239-53, January.
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  8. Omtzigt Pieter, 2002. "Automatic identification and restriction of the cointegration space," Economics and Quantitative Methods, Department of Economics, University of Insubria qf0213, Department of Economics, University of Insubria.
  9. Kevin D. Hoover & Stephen J. Perez, . "Truth and Robustness in Cross-country Growth Regressions," Department of Economics, California Davis - Department of Economics 01-01, California Davis - Department of Economics.
  10. Hans-Martin Krolzig, 2001. "General--to--Specific Reductions of Vector Autoregressive Processes," Computing in Economics and Finance 2001, Society for Computational Economics 164, Society for Computational Economics.
  11. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198283164, October.
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  13. Neil R. Ericsson & James G. MacKinnon, 2002. "Distributions of error correction tests for cointegration," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 5(2), pages 285-318, 06.
  14. David Hendry & Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Economics Series Working Papers 3, University of Oxford, Department of Economics.
  15. Savin, N.E., 1984. "Multiple hypothesis testing," Handbook of Econometrics, Elsevier, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 14, pages 827-879 Elsevier.
  16. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, Econometric Society, vol. 48(4), pages 817-38, May.
  17. S. Fukuda-Parr, 2003. "Editor's Introduction," Journal of Human Development and Capabilities, Taylor & Francis Journals, Taylor & Francis Journals, vol. 4(3), pages 323-324.
  18. Banerjee, Anindya & Hendry, David F, 1992. "Testing Integration and Cointegration: An Overview," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 225-55, August.
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