2007
DOI: 10.1111/j.1368-423x.2007.00220.x
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Robust estimators for the fixed effects panel data model

Abstract: Christophe Croux t K.U. LeuvenThe presence of outlying observations in panel data can affect the classical estimates in a dramatic way. Nevertheless the common practice seems to disregard the problem. The aim of this work is to study robust regression techniques in the fixed effects linear panel data framework. Robustness of the procedures is investigated by means of breakdown point computations and simulation experiments. A distinction between outlying blocks and cells in a panel is made. To show the potentia… Show more

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Cited by 103 publications
(111 citation statements)
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“…Rousseeuw and Leroy (1987) To deal with the presence of any of these types of outliers, Bramati and Croux (2007) propose two equally well performing estimators, the Within Groups Generalized M-…”
Section: Robust Estimation Of Linear Fixed Effects Panel Data Modelsmentioning
confidence: 99%
“…Rousseeuw and Leroy (1987) To deal with the presence of any of these types of outliers, Bramati and Croux (2007) propose two equally well performing estimators, the Within Groups Generalized M-…”
Section: Robust Estimation Of Linear Fixed Effects Panel Data Modelsmentioning
confidence: 99%
“…This also defines a direction for further research, by bringing the literature on robust estimation in panel data models to bear on measurement error, cf. Wagenvoort and Waldmann (2002) and Bramati and Croux (2007).…”
Section: Discussionmentioning
confidence: 99%
“…452) point out that "(i)n particular, development of robust procedures for panel-data and time-series models would be of major interest for applied economic research." A highly robust MM-estimator for panel data with fixed effects has been proposed recently by Bramati and Croux (2007). While a discussion of details of this estimator is beyond the scope of this paper the underlying idea is to center the series of observations for a firm in a similar way to what is generally done when applying the within transformation that is used to estimate a fixed effects model.…”
Section: Differences Across Quantiles Outliers and Unobserved Hetermentioning
confidence: 99%