BookmarkSubscribeRSS Feed
Camillus
Calcite | Level 5

Hi,

My question in in the subject.

PROC NLP proposes several methods to compute covariance of the parameter estimates. Some of them are really uncommon (at least in econometrics). I would like obtain some references in particular for the methods M, B and U. Thank you

4 REPLIES 4
sbxkoenk
SAS Super FREQ

Hello @Camillus ,

 

PROC NLP is really a SAS legacy procedure (from SAS 9.3).

Such that people know what you are talking about , I put here a link to the (9.3) doc:

SAS/OR(R) 9.3 User’s Guide: Mathematical Programming Legacy Procedures
https://support.sas.com/documentation/cdl/en/ormplpug/64004/HTML/default/viewer.htm#ormplpug_nlp_sec...

 

The same six (6) types of covariance matrices are still there in PROC OPTMODEL

( The Nonlinear Programming Solver PROC OPTMODEL ).

Any reason why you stick to PROC NLP?
You can migrate your PROC NLP code to PROC OPTMODEL.

 

BR,

Koen

Camillus
Calcite | Level 5

Thank you.

I'm used to using NLP and I don't know OPTMODEL. I am finishing an article using NLP. So I'm not going to change now but I'll look into that next time.

RobPratt
SAS Super FREQ

When you are ready to migrate, you might find this example helpful:

PROC NLP: Rewriting NLP Models for PROC OPTMODEL - 9.3 (sas.com)

sbxkoenk
SAS Super FREQ

Hello,

 

With regard to your original question , see :

Example 7.5: Approximate Standard Errors

at page 648 -- 653 ✦ Chapter 7: The NLP Procedure

https://support.sas.com/documentation/onlinedoc/or/132/nlp.pdf

 

Koen

SAS Innovate 2025: Call for Content

Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 25. Read more here about why you should contribute and what is in it for you!

Submit your idea!

Multiple Linear Regression in SAS

Learn how to run multiple linear regression models with and without interactions, presented by SAS user Alex Chaplin.

Find more tutorials on the SAS Users YouTube channel.

Discussion stats
  • 4 replies
  • 821 views
  • 2 likes
  • 3 in conversation