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An interpretation and implementation of the Theil-Goldberger 'mixed' estimator

Listed author(s):
  • Christopher F Baum


    (Boston College
    DIW Berlin)

Theil and Goldberger (Int. Ec. Rev., 1961) and Theil (JASA, 1963) proposed a generalized least squares approach to 'mixing' sample information and prior beliefs about the coefficients of a regression equation. Their 'mixed' estimator may be considered as a stochastic version of constrained least squares (Stata's cnsreg). Although based on frequentist statistics, the TG estimator is identical to that used in a Bayesian estimation approach when an informative prior density is employed (Greene text, 2008). It may also be viewed as a 'one-shot' application of the Kalman filter, providing an updating equation for point and interval coefficients based on prior and sample information (Cuthbertson et al. text, 1992). I discuss the motivation for the estimator and my implementation in Stata code, tgmixed, and give illustrations of how it might be usefully employed.

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Paper provided by Stata Users Group in its series CHI11 Stata Conference with number 14.

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Date of creation: 20 Jul 2011
Handle: RePEc:boc:chic11:14
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