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The Properties of Automatic Gets Modelling

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  • Hendry, David F

    (University of Oxford)

  • Hans-Martin Krolzig

Abstract

We describe some recent developments in PcGets, and consider their impact on its performance across different (unknown) states of nature. We discuss the consistency of its selection procedures, and 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. We also describe how PcGets has been extended to assist non-experts in model formulation, handle more variables than observations, and tackle non-linear models.

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Bibliographic Info

Paper provided by Royal Economic Society in its series Royal Economic Society Annual Conference 2003 with number 105.

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Date of creation: 04 Jun 2003
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Handle: RePEc:ecj:ac2003:105

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Keywords: Model selection; econometric methodology; PcGets; selection consistency; Monte Carlo;

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References

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  1. Kevin D. Hoover & Stephen J. Perez, . "Data Mining Reconsidered: Encompassing And The General-To-Specific Approach To Specification Search," Department of Economics 97-27, California Davis - Department of Economics.
  2. Neil R. Ericsson & James G. MacKinnon, 2000. "Distributions of Error Correction Tests for Cointegration," Econometric Society World Congress 2000 Contributed Papers 0561, Econometric Society.
  3. P. Dorian Owen, 2003. "General-to-Specific Modelling Using PcGets," Journal of Economic Surveys, Wiley Blackwell, vol. 17(4), pages 609-628, 09.
  4. Kevin Hoover & Harris Dellas, 2003. "Truth and Robustness in Cross-country Growth Regressions," Working Papers 11, University of California, Davis, Department of Economics.
  5. 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.
  6. Hans-Martin Krolzig, 2001. "General--to--Specific Reductions of Vector Autoregressive Processes," Computing in Economics and Finance 2001 164, Society for Computational Economics.
  7. Breusch, T.S. & Pagan, A.R., . "The Lagrange multiplier test and its applications to model specification in econometrics," CORE Discussion Papers RP -412, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  8. 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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  10. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
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  12. J. David Baldwin & Ronald L. Oaxaca, 2003. "Editors' Introduction," American Economic Review, American Economic Association, vol. 93(2), pages 7-7, May.
  13. S. Fukuda-Parr, 2003. "Editor's Introduction," Journal of Human Development and Capabilities, Taylor & Francis Journals, vol. 4(3), pages 323-324.
  14. Savin, N.E., 1984. "Multiple hypothesis testing," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 14, pages 827-879 Elsevier.
  15. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164, September.
  16. Omtzigt Pieter, 2002. "Automatic identification and restriction of the cointegration space," Economics and Quantitative Methods qf0213, Department of Economics, University of Insubria.
  17. Godfrey, Leslie G, 1978. "Testing for Higher Order Serial Correlation in Regression Equations When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1303-10, November.
  18. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
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