Communications for Statistical Applications and Methods

  • 제28권4호
  • /
  • Pages.315-327
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  • 2021
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Generalized Bayes estimation for a SAR model with linear restrictions binding the coefficients

  • 투고 : 2020.10.25
  • 심사 : 2021.03.12
  • 발행 : 2021.07.31
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초록

The Spatial Autoregressive (SAR) models have drawn considerable attention in recent econometrics literature because of their capability to model the spatial spill overs in a feasible way. While considering the Bayesian analysis of these models, one may face the problem of lack of robustness with respect to underlying prior assumptions. The generalized Bayes estimators provide a viable alternative to incorporate prior belief and are more robust with respect to underlying prior assumptions. The present paper considers the SAR model with a set of linear restrictions binding the regression coefficients and derives restricted generalized Bayes estimator for the coefficients vector. The minimaxity of the restricted generalized Bayes estimator has been established. Using a simulation study, it has been demonstrated that the estimator dominates the restricted least squares as well as restricted Stein rule estimators.

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과제정보

The authors are grateful to the Editor and three anonymous referees for their useful comments and suggestions.

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