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Evaluating Automatic Model Selection

Listed author(s):
  • Castle Jennifer L.

    (University of Oxford)

  • Doornik Jurgen A

    (University of Oxford)

  • Hendry David F.

    (University of Oxford)

We outline a range of criteria for evaluating model selection approaches that have been used in the literature. Focusing on three key criteria, we evaluate automatically selecting the relevant variables in an econometric model from a large candidate set. General-to-specific selection is outlined for a regression model in orthogonal variables, where only one decision is required to select, irrespective of the number of regressors. Comparisons with an automated model selection algorithm, Autometrics (Doornik, 2009), show similar properties, but not restricted to orthogonal cases. Monte Carlo experiments examine the roles of post-selection bias corrections and diagnostic testing as well as evaluate selection in dynamic models by costs of search versus costs of inference.

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Article provided by De Gruyter in its journal Journal of Time Series Econometrics.

Volume (Year): 3 (2011)
Issue (Month): 1 (February)
Pages: 1-33

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Handle: RePEc:bpj:jtsmet:v:3:y:2011:i:1:n:8
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  2. Kevin D. Hoover & Stephen J. Perez, "undated". "Truth and Robustness in Cross-country Growth Regressions," Department of Economics 01-01, California Davis - Department of Economics.
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  4. Julia Campos & David F. Hendry & Hans-Martin Krolzig, 2003. "Consistent Model Selection by an Automatic "Gets" Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 803-819, December.
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