SAS vs Stata Jackknife

[BruceBrad]

I'm getting different results when using SAS (9.4) and Stata (v11) for jackknife regression. Anyone know why these are different?

Below are the key parts of my SAS and Stata code. If I run surveyreg using the default Taylor series linearisation, and also run the corresponding Stata code on the same data, I get (near enough to) identical results. When I use the jackknife approach, the std errors are essentially the same as the Taylor series approach in Stata, but about 10% larger in SAS.

SAS CODE

title "Regression: Taylor series linearisation";

proc surveyreg data=temp1 total=lsac.stratum74with73   ;

   strata stratum74with73;

   cluster pcodes;

   weight defwt;

   model ready5_as = edl edm edh /noint;

run;

title "Regression: Jackknife method (10% larger SEs)";

proc surveyreg data=temp1 varmethod=jk ;

   strata stratum74with73;

   cluster pcodes;

   weight defwt;

   model ready5_as = edl edm edh /noint;

run;

STATA CODE

svyset pcodes [pweight=defwt], strata(stratum74with73) fpc(totalstratums)

* Taylor series linearisation

svy: regress ready5_as edl edm edh, noconstant

* Jackknife calculation

svy jackknife: regress ready5_as edl edm edh, noconstant

[BruceBrad]

I think it has something to do with the FPC. If I remove the FPC information from the code, all four approaches give essentially the same results (the larger std errors). But presumably the optimal estimates should take account of this? The SAS documentation implies that the jackknife estimate doesn't, and shouldn't, take account of the FPC, but it would seem the Stata code does in some way. Which is correct?