Raising the bar (20)

Paul Elhorst, Maria Abreu, Pedro Amaral, Arnab Bhattacharjee, Steven Bond-Smith, Coro Chasco, Luisa Corrado, Jan Ditzen, Daniel Felsenstein, Franz Fuerst, Philip McCann, Vassilis Monastiriotis, Francesco Quatraro, Umed Temursho, Jihai Yu

Research output: Contribution to journalEditorial


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.

Original languageAmerican English
Pages (from-to)151-155
Number of pages5
JournalSpatial Economic Analysis
Issue number2
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 Regional Studies Association.


  • logit
  • machine learning
  • prediction
  • spatial econometrics
  • urban economics


Dive into the research topics of 'Raising the bar (20)'. Together they form a unique fingerprint.

Cite this