Useful matrix transformations for panel data analysis: a survey

B. H. Baltagi

doi:10.1007/BF02925550

Useful matrix transformations for panel data analysis: a survey

B. H. Baltagi

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	281-301
Number of pages	21
Journal	Statistical Papers
Volume	34
Issue number	1
DOIs	https://doi.org/10.1007/BF02925550
State	Published - Dec 1 1993
Externally published	Yes

Keywords

error components models
spectral decomposition
weighted least squares

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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