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Evaluating Automatic Model Selection

Listed author(s):
  • Jennifer Castle
  • David Hendry
  • Jurgen A. Doornik

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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File URL: http://www.economics.ox.ac.uk/materials/papers/4217/paper474.pdf
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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
Handle: RePEc:oxf:wpaper:474
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  7. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, October.
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  15. 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.
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