Dear Statalist,
I'm having trouble with the svy jackknife command. I had earlier implemented a crewd jackknife estimator myself, tailored for my particular complex survey design including both stratification and multistage cluster sampling. With Stata 9 I presumably should need to use this homemade program anymore, since svy jackknife should do the job for me. However, the results from my estimator and svy jackknife differs for reasons I am not quite clear of.
To take this down to a more concrete level, I tested the two commands on a small subsample of my survey, using only 2 strata with 2 PSU:s each. I then ran a simple regression, with the following estimates per replication (where b(h,j)=the regression coefficient received when excluding PSU j from stratum h):
b(1,1)=.4230769
b(1,2)=.5417409
b(2,1)=.5537783
b(2,2)=.4259508
The estimate from the entire sample is: b=.4866513
Plugging in these estimates into the formula for the mse estimator (Survey Data Manual, p. 266) yields:
1/2*[(.4230769-.4866513)^2+(.5417409-.4866513)^2]+1/2*[(.5537783-.4866513)^2+(.4259508-.4866513)^2]
which is aproximately equal to .0076336. The square root of this, that is, the estimate of the standard error is: .0873703.
Incidentally, this is what my homemade jackknife estimator arrives at. However, svy jackknife reaches a somewhat different conclusion: se = .1070064
This is so despite the fact that the same estimated b(h,j)-coefficients go into both procedures (I have checked this by running jackknife noisily). There also appears to be nothing wrong with the weights: the "sum of wgt is..." yields exactly similar results.
So what could be wrong? What could explain the difference?
All the best,
Jan Teorell
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