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The Properties of Automatic GETS Modelling* 


  • * 

    Sargan Lecture of the Royal Economic Society, 2004. We are indebted to Julia Campos, Dorian Owen and Jon Temple for helpful comments and suggestions. Financial support to the first author from the UK Economic and Social Research Council under a Professorial Research Fellowship, RES 015 270035, and grant RES 000 230539, is gratefully acknowledged.


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.