Double Autocorrelation in Two Way Error Component Models

Brou, Jean Marcelin Bosson; Kouassi, Eugène; Kymn, Kern O.

doi:10.4236/ojs.2011.13022

Cited by 2 publications

(8 citation statements)

References 19 publications

(9 reference statements)

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“…Section 4 takes up some of these commonly used structures and describes how a solution can be found for these structures using our new decomposition result. Section 5 presents a new transformation along the lines of [2]. Finally, we conclude by pointing out some interesting aspects of our approach that may be worth investigating further.…”

Section: N N mentioning

confidence: 99%

“…In our note, we use our methodology to extend their approach to more general structures at the cross-sectional level. We derive the spectral decomposition as well as the determinant of the resulting variance-covariance matrix whereas [2] only derives the inverse of the transformed variance-covariance matrix.…”

Section: N N mentioning

confidence: 99%

“…e sam atio Here one can use th vious point (due to e transform a 2), i.e., n as in the Lemm pre 2 , N b…”

Section: Some Special Casesmentioning

confidence: 99%

“…Another more recent study [2] presents a general approach for a two-way EC under double autocorrelation in both the time effect and the idiosyncratic error. They propose a transformation based on a mixture of Cholesky and spectral decompositions as a first stage (what we call a "hybrid" transformation in this note), and a spectral decomposition as a second stage.…”

Section: N N mentioning

confidence: 99%

“…In general, the variance-covariance matrices of the two autocorrelated processes do not commute. [2] proposes a 3-stage transformation that circumvents the double autocorrelation problem. They successively apply the Cholesky and the spectral transformations to obtain a simpler structure for which they are able to give the inverse.…”

Section: Extension To "Hybrid" Transformationsmentioning

confidence: 99%

See 4 more Smart Citations

General Variance Covariance Structures in Two-Way Random Effects Models

Porres¹,

Krishnakumar²

2013

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This paper examines general variance-covariance structures for the specific effects and the overall error term in a twoway random effects (RE) model. So far panel data literature has only considered these general structures in a one-way model and followed the approach of a Cholesky-type transformation to bring the model back to a "classical" one-way RE case. In this note, we first show that in a two-way setting it is impossible to find a Cholesky-type transformation when the error components have a general variance-covariance structure (which includes autocorrelation). Then we propose solutions for this general case using the spectral decomposition of the variance components and give a general transformation leading to a block-diagonal structure which can be easily handled. The results are obtained under some general conditions on the matrices involved which are satisfied by most commonly used structures. Thus our results provide a general framework for introducing new variance-covariance structures in a panel data model. We compare our results with [1] and [2] highlighting similarities and differences.

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Section: N N mentioning

confidence: 99%

Section: N N mentioning

confidence: 99%

“…e sam atio Here one can use th vious point (due to e transform a 2), i.e., n as in the Lemm pre 2 , N b…”

Section: Some Special Casesmentioning

confidence: 99%

Section: N N mentioning

confidence: 99%

Section: Extension To "Hybrid" Transformationsmentioning

confidence: 99%