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The Properties of Automatic "GETS" Modelling

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  • David F. Hendry
  • Hans-Martin Krolzig

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 "a priori" restrictions. Copyright 2005 Royal Economic Society.

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Bibliographic Info

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. Hans-Martin Krolzig, 2001. "General--to--Specific Reductions of Vector Autoregressive Processes," Computing in Economics and Finance 2001 164, Society for Computational Economics.
  2. Neil R. Ericsson & James G. MacKinnon, 1999. "Distributions of error correction tests for cointegration," International Finance Discussion Papers 655, Board of Governors of the Federal Reserve System (U.S.).
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