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Sub-sample Model Selection Procedures in Gets Modelling

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Author Info
David F. Hendry () (Dept of Economics, and Nuffield College, Oxford University)
Hans-Martin Krolzig () (Department of Economics, and Nuffield College, Oxford University)

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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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Publisher 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
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Handle: RePEc:nuf:econwp:0317

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

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  1. 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. [Downloadable!] (restricted)
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  2. Hendry, David F & Hans-Martin Krolzig, 2003. "The Properties of Automatic Gets Modelling," Royal Economic Society Annual Conference 2003 105, Royal Economic Society. [Downloadable!]
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  3. 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.
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  4. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February. [Downloadable!] (restricted)
  5. Kevin D. Hoover & Stephen J. Perez, . "Truth and Robustness in Cross-country Growth Regressions," Department of Economics 01-01, California Davis - Department of Economics. [Downloadable!]
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  6. Hans-Martin Krolzig, 2003. "General-to-Specific Model Selection Procedures for Structural Vector Autoregressions," Economics Papers 2003-W15, Economics Group, Nuffield College, University of Oxford. [Downloadable!]
  7. Hans-Martin Krolzig, 2001. "General--to--Specific Reductions of Vector Autoregressive Processes," Computing in Economics and Finance 2001 164, Society for Computational Economics. [Downloadable!]
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