A comprehensive, modular and flexible framework is described for estimation of robust standard errors in panel data. Heteroskedasticity and autocorrelation robust estimators are brought together with the SCC mixing-fields based estimator, the unconditional PCSE estimator and the recent double-clustering approach, trying to bring together the applied literatures in macroeconometrics, finance, political science and accounting by demonstrating the common features of these apparently different approaches. The covariance estimators are integrated in the R package 'plm' and allow robust specification and restriction testing over a number of different panel models.
Robust standard errors for panel data: a general, software-oriented framework
Millo G
2014-01-01
Abstract
A comprehensive, modular and flexible framework is described for estimation of robust standard errors in panel data. Heteroskedasticity and autocorrelation robust estimators are brought together with the SCC mixing-fields based estimator, the unconditional PCSE estimator and the recent double-clustering approach, trying to bring together the applied literatures in macroeconometrics, finance, political science and accounting by demonstrating the common features of these apparently different approaches. The covariance estimators are integrated in the R package 'plm' and allow robust specification and restriction testing over a number of different panel models.Pubblicazioni consigliate
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