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The Properties of Automatic GETS Modelling

30 Pages Posted: 7 Mar 2005  

David F. Hendry

University of Oxford - Department of Economics

Hans-Martin Krolzig

Humboldt University of Berlin - Institute for Statistics and Econometrics

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Abstract

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 priori restrictions.

Suggested Citation

Hendry, David F. and Krolzig, Hans-Martin, The Properties of Automatic GETS Modelling. Economic Journal, Vol. 115, No. 502, pp. C32-C61, March 2005. Available at SSRN: https://ssrn.com/abstract=678423

David F. Hendry (Contact Author)

University of Oxford - Department of Economics ( email )

Manor Road Building
Manor Road
Oxford, OX1 3BJ
United Kingdom
+44 1865 278544 (Phone)
+44 1865 278557 (Fax)

Hans-Martin Krolzig

Humboldt University of Berlin - Institute for Statistics and Econometrics ( email )

Spandauer Str. 1
Berlin, D-10178
Germany

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