Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Jun Xu <mystata@hotmail.com> |

To |
Listserv STATA <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: gradient and the inverse of the information matrix |

Date |
Fri, 3 May 2013 16:15:47 -0500 |

Chi-lin, Along with suggestions from Maarten, your paper nicely demonstrates how to tweak Stata codes to conduct the LM test as well as user-written ado files for the same thing. Although there are a few papers floating around out there that provide some expository discussions, like the one by Buse (1982) and others, none of them has the empirical technicalities in your paper. By the way, are those two commands readily downloadable? Thanks a lot! Jun ---------------------------------------- > From: kirin_guess@yahoo.com.tw > To: statalist@hsphsun2.harvard.edu > Subject: Re: st: gradient and the inverse of the information matrix > Date: Fri, 3 May 2013 13:23:39 +0100 > > I have a manuscript that is exactly related to your question. The article > also briefs some possible reasons why the score test conducted by SAS and > STATA can be different. (See the footnote 6.) > You can download the article from: > https://docs.google.com/file/d/0B984NoKuZv46akZPeGRKcTZHekU/edit?usp=sharing > > Chi-lin Tsai > > > > -----Original Message----- > From: Jon Mu > Sent: Wednesday, May 01, 2013 10:01 PM > To: Listserv STATA > Subject: st: gradient and the inverse of the information matrix > > Hi Statalisters, > > I am trying to check into the (Rao's) score (or commonly known as the > Lagrange Multiplier) test for a model that I am working on. I got results > from SAS already, and I want to see if those from SAS would square with the > one produced from my own Stata codes. > > They don't match, and looks like I probably made some mistakes in my Stata > codes. For the generalized formula to get the Chi-Square statistic, I need > to get the gradient and the inverse of the information matrix. For the > inverse of the information matrix, I can grab from e(V) directly without any > further calculation. > > So I might've made some mistake in the gradient. I've searched through the > voluminous Stata pdf documentation using gradient as the key word, and I was > not able to find useful information. But I vaguely remember a while back ago > when I was also checking into related issues, I read somewhere that the > e(gradient) matrix is a gradient with respect to xb, not b, so I suspect > that might be the cause. I am wondering if that's the case. If I am right on > this, then a follow-up question is how to recover the gradient with respect > to b since I feel there might not be a linear transformation that I can use > to get it directly. Any input/suggestion would be appreciated. > > Jun Xu, PhD > Associate Professor > Department of Sociology > Ball State University > Muncie, IN 46037 > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/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/faqs/resources/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/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: gradient and the inverse of the information matrix***From:*Jon Mu <mystata@hotmail.com>

**Re: st: gradient and the inverse of the information matrix***From:*<kirin_guess@yahoo.com.tw>

- Prev by Date:
**st: prediction after y-logged oheckman regression** - Next by Date:
**RE: st: gradient and the inverse of the information matrix** - Previous by thread:
**Re: st: gradient and the inverse of the information matrix** - Next by thread:
**RE: st: gradient and the inverse of the information matrix** - Index(es):