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The Properties of Automatic "GETS" Modelling

  • David F. Hendry
  • Hans-Martin Krolzig

After reviewing the simulation performance of general-to-specific automatic regression-model selection, as embodied in "PcGets", we show how model selection can be non-distortionary: approximately unbiased 'selection estimates' are derived, with reported standard errors close to the sampling standard deviations of the estimated DGP parameters, and a near-unbiased goodness-of-fit measure. The handling of theory-based restrictions, non-stationarity and problems posed by collinear data are considered. Finally, we consider how "PcGets" can handle three 'intractable' problems: more variables than observations in regression analysis; perfectly collinear regressors; and modelling simultaneous equations without "a priori" restrictions. Copyright 2005 Royal Economic Society.

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Article provided by Royal Economic Society in its journal The Economic Journal.

Volume (Year): 115 (2005)
Issue (Month): 502 (03)
Pages: C32-C61

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Handle: RePEc:ecj:econjl:v:115:y:2005:i:502:p:c32-c61
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  1. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
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