Working paper

A Hotelling spatial scan statistic for functional data: application to economic and climate data

Zaineb Smida, Thibault Laurent, and Lionel Cucala

Abstract

A scan method for functional data indexed in space has been developed. The scan statistic is derived from the Hotelling test statistic for functional data, extending the univariate and multivariate Gaussian spatial scan statistics. This method consistently outperforms existing techniques in detecting and locating spatial clusters, as demonstrated through simulations. It has been applied to two types of real data: economic data in order to identify spatial clusters of abnormal unemployment rates in Spain and climatic data in order to detect unusual climate change patterns in Great Britain, Nigeria, Pakistan, and Venezuela.

Keywords

Cluster detection, Functional data, Hotelling T2 test, Spatial Scan statistic.;

JEL codes

  • C12: Hypothesis Testing: General
  • C21: Cross-Sectional Models • Spatial Models • Treatment Effect Models • Quantile Regressions
  • E24: Employment • Unemployment • Wages • Intergenerational Income Distribution • Aggregate Human Capital
  • Q54: Climate • Natural Disasters • Global Warming

Reference

Zaineb Smida, Thibault Laurent, and Lionel Cucala, “A Hotelling spatial scan statistic for functional data: application to economic and climate data”, TSE Working Paper, n. 24-1583, October 2024.

See also

Published in

TSE Working Paper, n. 24-1583, October 2024