Daniel Hoechle. University of Basel. The Stata Journal (2007) 7, Number 3, pp. 281–312
Wednesday 14 June 2023, by Carlos San Juan
This paper may be useful for a student dealing with panel data using Stata as describes the xtscc program syntax to estimate Driscoll and Kraay (1998) standard errors
In social sciences, and particularly in economics, analyzing large-scale microeconometric panel datasets has become common. Compared with purely cross-sectional data, panels are attractive since they often contain far more information than single cross-sections and thus allow for an increased precision in estimation. Unfortunately, however, actual information of microeconometric panels is often overstated since microeconometric data are likely to exhibit all sorts of cross-sectional and temporal dependencies.
In the words of Cameron and Trivedi (2005, 702), “NT correlated observations have less information than NT independent observations.” Therefore, erroneously ignoring possible correlation of regression disturbances over time and between subjects can lead to biased statistical inference.
To ensure validity of the statistical results, most recent studies that include a regression on panel data therefore adjust the standard errors of the coefficient estimates for possible dependence in the residuals.
However, according to Petersen (2007), many recently published articles in leading finance journals still fail to adjust the standard errors appropriately. Furthermore, although most empirical studies now provide standard error estimates that are heteroskedasticity- and autocorrelation consistent, cross-sectional or “spatial” dependence is still largely ignored.
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