Raising the bar (20). Issue 2 (3rd April 2022)

• Record Type: Journal Article
• Authors: Elhorst, Paul; Abreu, Maria; Amaral, Pedro; Bhattacharjee, Arnab; Bond-Smith, Steven; Chasco, Coro; Corrado, Luisa; Ditzen, Jan; Felsenstein, Daniel; Fuerst, Franz; McCann, Philip; Monastiriotis, Vassilis; Quatraro, Francesco; Temursho, Umed; Yu, Jihai
• Is Part Of: Spatial economic analysis. Volume 17:Issue 2(2022)
• Page Start: 151
• Page End: 155
• Publication Date: 2022-04-03
• Subjects: machine learning -- prediction -- spatial econometrics -- logit -- urban economics; C21 -- C52 -- C67 -- G33 -- R11 -- Z32; 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: 10.1080/17421772.2022.2053402
• Languages: English
• ISSNs: 1742-1772

Abstract

ABSTRACT: This editorial summarizes the papers published in issue 17 (2) (2022). The first paper evaluates logistic regression and machine-learning methods for predicting firm bankruptcy. The second paper demonstrates that machine learning outperforms existing tools to improve the estimation of regional input–output tables. The third paper investigates whether network centrality depends on the probability that a tie between two nodes is formed, as well as its intensity. The fourth paper sets out a Bayesian estimation technique to estimate a spatial autoregressive multinomial logit model. The fifth paper develops a statistic to test for several misspecification problems in spatial econometric models. The sixth paper compares the prediction accuracy of spatial and non-spatial econometric models explaining the number of tourist arrivals across countries.