TY - JOUR
T1 - Raising the bar (20)
AU - Elhorst, Paul
AU - Abreu, Maria
AU - Amaral, Pedro
AU - Bhattacharjee, Arnab
AU - Bond-Smith, Steven
AU - Chasco, Coro
AU - Corrado, Luisa
AU - Ditzen, Jan
AU - Felsenstein, Daniel
AU - Fuerst, Franz
AU - McCann, Philip
AU - Monastiriotis, Vassilis
AU - Quatraro, Francesco
AU - Temursho, Umed
AU - Yu, Jihai
N1 - Publisher Copyright:
© 2022 Regional Studies Association.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - logit
KW - machine learning
KW - prediction
KW - spatial econometrics
KW - urban economics
UR - http://www.scopus.com/inward/record.url?scp=85129676982&partnerID=8YFLogxK
U2 - 10.1080/17421772.2022.2053402
DO - 10.1080/17421772.2022.2053402
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AN - SCOPUS:85129676982
SN - 1742-1772
VL - 17
SP - 151
EP - 155
JO - Spatial Economic Analysis
JF - Spatial Economic Analysis
IS - 2
ER -