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Re: st: xtscc versus cluster: was Heteroskedasticity--help
Christopher wonders why it can happen that in some cases the -xtscc-
program produces standard error estimates that are (much) smaller than
panel robust standard errors. In his case, the panel consists of n=348
cross-sectional units and t=9 periods over time and the average
--absolute-- correlation between the residuals of two cross-sectional
units is 0.43.
Such a situation can arise if the residuals between two
cross-sectional units are on average negatively correlated. Why? While
NT positively correlated observations possess less information than NT
independent observations, NT negatively correlated observations
possess --more-- information than NT independent observations (from a
statistical point of view). Unfortunately, however, for most economics
applications it is difficult to convincingly argue why the subjects of
a panel should on average be negatively correlated with each other.
Christopher could estimate his panel regression with time-fixed
effects (which implements a parametric correction for cross-sectional
dependence) and compare the results from this regression to those of
the -xtscc- command and to the results of estimating the regression
without time-fixed effects. The advantage of time-fixed effects in his
specific case is that a time-dimension of t=9 is relatively short if
one considers the fact that Driscoll-Kraay standard errors rely on
large T asymptotics.
As a pragmatic solution, I would suggest the following: If the
standard error estimates of the -xtscc- program (with short lag
length) turn out to be much smaller than the panel robust standard
errors (which I think is unlikely to happen for medium- and
large-scale microeconometric panels but which can happen for smaller
panels with relatively few observations over time), then I would stay
on the safe side and rely on the more conservative SE estimates which
in this case are the panel robust standard errors.
I hope this helps.
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