Raising the bar (11). Issue 2 (3rd April 2019)

• Record Type: Journal Article
• Authors: Elhorst, Paul; Abreu, Maria; Amaral, Pedro; Bhattacharjee, Arnab; Corrado, Luisa; Doran, Justin; Fuerst, Franz; Gallo, Julie Le; McCann, Philip; Monastiriotis, Vassilis; Quatraro, Francesco; Yu, Jihai
• Is Part Of: Spatial economic analysis. Volume 14:Issue 2(2019)
• Page Start: 129
• Page End: 132
• Publication Date: 2019-04-03
• Subjects: regional science -- discrimination -- real estate -- spatio-temporal models; C21 -- C33 -- J71 -- R1 -- R3; 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: 10.1080/17421772.2019.1597958
• Languages: English
• ISSNs: 1742-1772

Abstract

ABSTRACT: This editorial summarizes the papers published in issue 14(2) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper deals with past and current challenges for regional science research. The second paper investigates whether people living in deprived neighbourhoods have less chance of succeeding in a job application. The third paper finds evidence that real estate firms can avoid price competition when market shares of their allies increase in the vicinity. The fourth paper is methodological: it considers a spatial autoregressive (SAR) model with heterogeneous coefficients and extensively analyzes the impact of this extension on the direct and indirect effects estimates. The fifth paper proposes an innovative method to estimate the elements of the spatial weight matrix in a spatial econometric model. The final paper is econometric–theoretical: it proposes a new generalized method of moments (GMM) estimator of the coefficients of a SAR model if the error terms are heteroskedastic of an unknown form.