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Re: st: Performance of ivregress vs ivreg


From   "Brian P. Poi" <[email protected]>
To   [email protected]
Subject   Re: st: Performance of ivregress vs ivreg
Date   Tue, 11 Nov 2008 16:21:59 -0600 (CST)

On Tue, 11 Nov 2008, Davide Cantoni wrote:

Dear statalisters,

I have recently moved to Stata 10 and I am surprised about the
replacement of -ivreg- with -ivregress-. While I appreciate some of
the additional options (which would formerly be available only in
-ivreg2-), I am puzzled by the generally strictly inferior performance
of -ivregress-. I could not find any discussion of this in the
Statalist so far, which is slightly surprising to me. Here are my
qualms.

1. Memory usage. -ivregress- needs much more memory than -ivreg-. So
you need to -set mem- to a much higher level than before.

2. Speed. -ivregress 2sls- is much slower than -ivreg-. I did a small
simulation: -ivreg- took 4 seconds, -ivregress 2sls- took 18 seconds.
That's an increase of 350%!


These two points represent trade-offs between raw speed and features. While -ivreg- is somewhat faster than -ivregress-, the newer -ivregress- offers many features not available with -ivreg-, including support for LIML and GMM estimators and a host of postestimation utilities. Given the increased awareness of problems with weak instruments, validity of overidentifying restrictions, and other issues, the benefits of having the flexibility afforded by -ivregress- outweigh the speed advantage of -ivreg-.

If speed is most important and you do not want to use the postestimation commands, then you could continue to use the undocumented -ivreg- command.

3. Finally I keep getting the following error message, which I can't
explain myself:
   option noline not allowed
   r(198);


We have never seen this error appear with -ivregress-. Make sure your copy of Stata is up-to-date by typing in -update all- and following the instructions on the screen. If you continue to receive this error message, send an email to [email protected] including the exact syntax you are using along with a dataset to replicate the problem.

   -- Brian Poi
   -- [email protected]
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