regression coefficient confidence intervals and degrees of freedom

Hi,

I'd like to compute regression coefficient confidence intervals and the
timeseries I'm examining have significant autocorrelation. The
confidence intervals depend on the degrees of freedom (df). The page at
http://www.ncl.ucar.edu/Document/Functions/Built-in/regline.shtml states
that df should be adjusted as

df = N * (1 - acr) / (1 + acr)

where acr is the lag-1 autocorrelation of the dependent variable,
assuming that this autocorrelation is statistically significant.

A message on ncl-talk, archived at
http://www.ncl.ucar.edu/Support/talk_archives/2011/1866.html suggests
for the related problem of significance testing of the regression
coefficient to use the ncl function equiv_sample_size to compute the df.

For the timeseries I'm examining, equiv_sample_size yields 84, and the
formula given with the regline documentation yields 90.5. These values
are different, though not dramatically so.

Is there a reason to prefer one of these df computations over the other
in the computation of regression coefficient confidence intervals?

(I've browsed the Zwiers & von Storch paper mentioned in the
equiv_sample_size documentation, but it is over my head.)

Thanks, Keith
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Received on Wed Mar 21 21:30:26 2012