Dear Jeff,
Many thanks for your very clear explanation! Now I understand why the two reported standard errors are different. You are right I should use method (1) since I am interested in the jackknife standard error of delta.
Many thanks again, your help is very valuable!
Best regards,
Marc
----- Original Message ----
From: "Jeff Pitblado, StataCorp LP" <[email protected]>
To: [email protected]
Sent: Tuesday, May 5, 2009 1:46:12 PM
Subject: Re: st: Jackknife and standard error in NEGBIN model
Marc Philipp <[email protected]> is using the -jackknife:- prefix command
with -nbreg-, and asks why the reported standard errors differ for the 'delta'
parameter between two different -jackknife:- specifications:
> I have a problem with the jackknife command. Hopefully there are some
> experienced users who will be able to help me. I am estimating a negative
> binomial model (NEGBIN 1), regressing a count variable y on a continuous
> variable x and on some other control variables z1, z2, ...
>
> Since I am only interested in the parameter of x and in the overdispersion
> parameter delta, I specified the command in this way:
>
> jackknife _b[x] e(delta), cluster(t): nbreg y x z*, dispersion(constant)
> nocons
>
> However, I observed that if I specify the command in this way, without
> collecting the two parameters I am interested in:
>
> jackknife, cluster(tt): nbreg y x z*, dispersion(constant) nocons,
>
> something strange happens: the estimated parameters are exactly the same,
> but the jackknife standard error of delta is completely different, much
> higher than in the previous case, whereas the jackknife standard error of
> b[x] is exactly the same.
>
> I read the Stata user guide and scanned the web to find some hints, but
> unsuccessfully. I don't understand why the standard error of the
> overdispersion parameter is so different, and don't know which command I
> should use.
>
> Have you already encountered such a problem with the jackknife command?
>
> Many thanks in advance for your help!
Marc is using -jackknife:- in the following two ways
(1) . jackknife _b[x] e(delta), cluster(tt): nbreg y x z*, disp(c) nocons
(2) . jackknife, cluster(tt): nbreg y x z*, disp(c) nocons
and wants to know why the standard error for 'delta' is bigger in (2) than in
(1).
In (1), -jackknife:- works with -e(delta)- directly; where -e(delta)- is
generated by
ereturn scalar delta = exp(_b[/lndelta])
so the reported standard error comes from the Jackknife replication method.
In (2), -jackknife:- works with -_b[/lndelta]- (the natural log of 'delta')
directly, then uses a standard transformation result to get the standard error
of 'delta' (coincidentally, this transformation is typically known as the
delta-method and has nothing special to do with our 'delta'). Thus the
standard error for the reported value of 'delta' in (2) is computed as
abs(_b[/lndelta])*SE(_b[/lndelta])
where 'SE(_b[/lndelta])' was computed via the Jackknife replication method.
If Marc really meant to compute the jackknife standard error of 'e(delta)',
then he should use (1).
Stata always uses the delta-method for computing standard errors for derived
ancillary parameters like 'delta'.
--Jeff
[email protected]
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
