Bayesian Estimation of Spatial Regression Models with Skew-normally Covariates Measured with Errors: Evidence from Monte Carlo Simulations

Spatial data are susceptible to covariates measured with errors. However, the errorprone covariates and the random errors are usually assumed to be symmetrically, normally distribution. The purpose of this paper is to analyze Bayesian inference of spatial regression models with a covariate measured with Skew-normal error by way of Monte Carlo simulation. We consider the spatial regression models with different degree of spatial correlation in the covariate of interest and measurement error variance. The simulation examines the performance of Bayesian estimators in the case of (i) Naive models without measurement error correction; (ii) Normal distribution for the error-prone covariate and random errors; (iii) Skew-normal distribution (SN) for the error-prone covariate and normal distribution for random errors. We use the relative bias (RelBias) and Root Mean Squared Error (RMSE) as valuation criteria. The main result is that the Skew-normal prior estimator outperform the normal, symmetrical prior distribution and the Naive models without measurement error correction.     Keywords: Spatial regression, measurement error, Bayesian analysis, Skew-normal distribution