A Bayesian Approach to Parameter Estimation in the Presence of Spatial Missing Data. Issue 2 (2nd April 2016)

• Record Type: Journal Article
• Authors: Panzera, Domenica; Benedetti, Roberto; Postiglione, Paolo
• Is Part Of: Spatial economic analysis. Volume 11:Issue 2(2016)
• Page Start: 201
• Page End: 218
• Publication Date: 2016-04-02
• Subjects: Spatial data -- missing values -- Bayesian Interpolation Method -- multiple imputation; C10 -- C11 -- O40 -- R10; Space in economics -- Periodicals; Economic geography -- Periodicals; Space in economics -- Econometric models -- Periodicals; Urbanization -- Periodicals; Espace (Économie politique) -- Périodiques; Économétrie -- Périodiques; Géographie économique -- Périodiques; Économie industrielle -- Périodiques; Économie du travail -- Périodiques; Économie urbaine -- Périodiques; Politique économique -- Périodiques; Relations économiques internationales -- Périodiques; 330.9
• DOI: 10.1080/17421772.2016.1102962
• Languages: English
• ISSNs: 1742-1772

Abstract

Abstract: The missing data problem has been widely addressed in the literature. The traditional methods for handling missing data may be not suited to spatial data, which can exhibit distinctive structures of dependence and/or heterogeneity. As a possible solution to the spatial missing data problem, this paper proposes an approach that combines the Bayesian Interpolation method [Benedetti, R. & Palma, D. (1994) Markov random field-based image subsampling method, Journal of Applied Statistics, 21(5), 495–509] with a multiple imputation procedure. The method is developed in a univariate and a multivariate framework, and its performance is evaluated through an empirical illustration based on data related to labour productivity in European regions.