Ridge-Type Pretest and Shrinkage Estimation Strategies in Spatial Error Models with an Application to a Real Data Example

Marwan Al-Momani () and Mohammad Arashi
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Marwan Al-Momani: Department of Mathematics, College of Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
Mohammad Arashi: Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran

Mathematics, 2024, vol. 12, issue 3, 1-19

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.

Keywords: spatial error model; asymptotic performance; bootstrapping; pretest; ridge estimator; shrinkage (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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