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Evaluating Automatic Model Selection

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  • Jennifer Castle
  • David Hendry
  • Jurgen A. Doornik

Abstract

We evaluate automatically selecting the relevant variables in an econometric model from a large candidate set.� General-to-specific selection is outlined for a constant model in orthogonal variables, where only one decision is required to select, irrespective of the number of regressors (N < T) where T is the sample size, then evaluated in simulation experiments for N = 1000.� Comparisons with 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, and evaluate Autometrics'�capability in dynamic models by its cost of search versus costs of inference.

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Bibliographic Info

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 474.

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Date of creation: 01 Jan 2010
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Handle: RePEc:oxf:wpaper:474

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Keywords: Model selection; Autometrics; Post-selection bias correction; Costs of search; Costs of inference;

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  1. Jerzy Mycielski & Michal Kurcewicz, 2004. "A Specification Search Algorithm for Cointegrated Systems," Computing in Economics and Finance 2004, Society for Computational Economics 321, Society for Computational Economics.
  2. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, Elsevier, vol. 169(2), pages 239-246.
  3. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
  4. 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, University of Canterbury, Department of Economics and Finance 09/13, University of Canterbury, Department of Economics and Finance.
  5. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
  6. Leeb, Hannes & P tscher, Benedikt M., 2003. "The Finite-Sample Distribution Of Post-Model-Selection Estimators And Uniform Versus Nonuniform Approximations," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 19(01), pages 100-142, February.
  7. David Hendry & Søren Johansen & Carlos Santos, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, Springer, vol. 23(2), pages 337-339, April.
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