Generalized Bayes estimation for a SAR model with linear restrictions binding the coefficients

• Published in: Communications for statistical applications and methods Vol. 28; no. 4; pp. 315 - 327
• Main Authors: Chaturvedi, Anoop, Mishra, Sandeep
• Format: Journal Article
• Language: Korean
• Published: 한국통계학회 31.07.2021
• Subjects: generalized Bayes estimator; minimaxity; restricted least squares estimator; spatial autoregressive (SAR) model; Stein rule estimator; restricted least squares estimator; generalized Bayes estimator; spatial autoregressive (SAR) model; minimaxity; Stein rule estimator
• ISSN: 2287-7843

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.