Useful matrix transformations for panel data analysis: a survey

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


This paper surveys some useful matrix transformations which simplify the derivation of GLS as WLS in an error component model. This is particularly important for large panel data applications where brute force inversion of large data matrices may not be feasible. This WLS transformation is known in the literature as the Fuller and Baltese (1974) transformation and its extension to error component models with heteroscedasticity, serial correlation, unbalancedness as well as a set of seemingly unrelated regressions are considered.

Original languageEnglish (US)
Pages (from-to)281-301
Number of pages21
JournalStatistical Papers
Issue number1
StatePublished - Dec 1 1993
Externally publishedYes


  • error components models
  • spectral decomposition
  • weighted least squares

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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