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Evaluating Automatic Model Selection

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  • Castle Jennifer L.

    (University of Oxford)

  • Doornik Jurgen A

    (University of Oxford)

  • Hendry David F.

    (University of Oxford)

Abstract

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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Bibliographic Info

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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  1. Carlos Santos & David Hendry & Soren Johansen, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, vol. 23(2), pages 317-335, April.
  2. Jerzy Mycielski & Michal Kurcewicz, 2004. "A Specification Search Algorithm for Cointegrated Systems," Computing in Economics and Finance 2004 321, Society for Computational Economics.
  3. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
  4. Hannes Leeb & Benedikt M. Poetscher, 2000. "The Finite-Sample Distribution of Post-Model-Selection Estimators, and Uniform Versus Non-Uniform Approximations," Econometrics 0004001, EconWPA.
  5. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
  6. Jennifer L. Castle & Xiaochuan Qin & W. Robert Reed, 2009. "How To Pick The Best Regression Equation: A Review And Comparison Of Model Selection Algorithms," Working Papers in Economics 09/13, University of Canterbury, Department of Economics and Finance.
  7. 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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