Abstract:
Spatial regression models are widely available across several disciplines, such as functional
magnetic resonance imaging analysis, econometrics, and house price analysis. In nature,
sparsity occurs when a limited number of factors strongly impact overall variation. Sparse covariance
structures are common in spatial regression models. The spatial error model is a significant spatial
regression model that focuses on the geographical dependence present in the error terms rather than
the response variable. This study proposes an effective approach using the pretest and shrinkage
ridge estimators for estimating the vector of regression coefficients in the spatial error mode, considering
insignificant coefficients and multicollinearity among regressors. The study compares the
performance of the proposed estimators with the maximum likelihood estimator and assesses their
efficacy using real-world data and bootstrapping techniques for comparison purposes.
