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Raising the bar (20)

Author

Listed:
  • 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

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.

Suggested Citation

  • Paul Elhorst & Maria Abreu & Pedro Amaral & Arnab Bhattacharjee & Steven Bond-Smith & Coro Chasco & Luisa Corrado & Jan Ditzen & Daniel Felsenstein & Franz Fuerst & Philip McCann & Vassilis Monastirio, 2022. "Raising the bar (20)," Spatial Economic Analysis, Taylor & Francis Journals, vol. 17(2), pages 151-155, April.
    • Handle: RePEc:taf:specan:v:17:y:2022:i:2:p:151-155
    DOI: 10.1080/17421772.2022.2053402
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