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The Properties of Automatic Gets Modelling

  • David Hendry
  • Hans-Martin Krolzig

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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File URL: http://www.nuff.ox.ac.uk/economics/papers/2003/W14/dfhhmk03a.pdf
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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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Web page: http://www.economics.ox.ac.uk/
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  1. 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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