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Sub-sample Model Selection Procedures in Gets Modelling

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  • David F. Hendry

    ()
    (Dept of Economics, and Nuffield College, Oxford University)

  • Hans-Martin Krolzig

    ()
    (Department of Economics, and Nuffield College, Oxford University)

Abstract

When the DGP is nested in the model, PcGets delivers high performance selection across different (unknown) states of nature. One of its steps involves sub-sample post-selection assessment, and here we consider its properties and investigate its practical application. The simulation results show that conditional on retaining a variable, sub-sample information cannot discriminate between substantive and adventitious significance. The Monte Carlo experiments also reveal that the sub-sample selection method suggested by Hoover and Perez (1999) is dominated by procedures selecting only on full-sample evidence, when both approaches are evaluated at a given size. Nevertheless, although the sub-sample procedures do not result in a genuinely beneficial trade-off between size and power, they are particularly successful in controlling the size for selection problems that were previously seemed almost intractable.

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Bibliographic Info

Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2003-W17.

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Length: 17 pages
Date of creation: 16 Apr 2003
Date of revision:
Handle: RePEc:nuf:econwp:0317

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Web page: http://www.nuff.ox.ac.uk/economics/

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  1. Hans-Martin Krolzig & David Hendry, 1999. "Computer Automation of General-to-Specific Model Selection Procedures," Computing in Economics and Finance 1999, Society for Computational Economics 314, Society for Computational Economics.
  2. Hendry, David F & Hans-Martin Krolzig, 2003. "The Properties of Automatic Gets Modelling," Royal Economic Society Annual Conference 2003, Royal Economic Society 105, Royal Economic Society.
  3. Brüggemann, Ralf & Krolzig, Hans-Martin & Lütkepohl, Helmut, 2002. "Comparison of model reduction methods for VAR processes," SFB 373 Discussion Papers, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes 2002,80, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  4. Kevin D. Hoover & Stephen J. Perez, . "Data Mining Reconsidered: Encompassing And The General-To-Specific Approach To Specification Search," Department of Economics, California Davis - Department of Economics 97-27, California Davis - Department of Economics.
  5. Kevin D. Hoover & Stephen J. Perez, 2004. "Truth and Robustness in Cross-country Growth Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, Department of Economics, University of Oxford, vol. 66(5), pages 765-798, December.
  6. Hans-Martin Krolzig, 2003. "General-to-Specific Model Selection Procedures for Structural Vector Autoregressions," Economics Papers, Economics Group, Nuffield College, University of Oxford 2003-W15, Economics Group, Nuffield College, University of Oxford.
  7. 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.
  8. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198283164, October.
  9. 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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Cited by:
  1. Barhoumi, K. & Brunhes-Lesage, V. & Darné, O. & Ferrara, L. & Pluyaud, B. & Rouvreau, B., 2008. "Monthly forecasting of French GDP: A revised version of the OPTIM model," Working papers, Banque de France 222, Banque de France.

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