Blockwise Euclidean likelihood for spatio-temporal covariance models

Víctor Morales-Oñate, Federico Crudu, Moreno Bevilacqua

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


A spatio-temporal blockwise Euclidean likelihood method for the estimation of covariance models when dealing with large spatio-temporal Gaussian data is proposed. The method uses moment conditions coming from the score of the pairwise composite likelihood. The blockwise approach guarantees considerable computational improvements over the standard pairwise composite likelihood method. In order to further speed up computation, a general purpose graphics processing unit implementation using OpenCL is implemented. The asymptotic properties of the proposed estimator are derived and the finite sample properties of this methodology by means of a simulation study highlighting the computational gains of the OpenCL graphics processing unit implementation. Finally, there is an application of the estimation method to a wind component data set.

Original languageEnglish
Pages (from-to)176-201
Number of pages26
JournalEconometrics and Statistics
StatePublished - Oct 2021
Externally publishedYes


  • Composite likelihood
  • Euclidean likelihood
  • Gaussian random fields
  • OpenCL
  • Parallel computing


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