[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
leechtcn <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Log Likelihood for Linear Regression Models |

Date |
Thu, 30 Oct 2003 23:02:24 -0800 (PST) |

Thanks a lot it is very helpful for me --- David Greenberg <[email protected]> wrote: > Equation 1 is correct. The reason some writers drop > the second term is this. For most purposes, the > likelihood function or its log are not of interest > in themselves. One might want to compare two of > them, or one might want to find the values of > parameters that will maximize the likelihood > function. If one is comparing two likelihood > functions by taking the difference, constant terms > will drop out. If one is finding the values of > parameters that will maximize the likelihood > function or its log, one will take the first > derivative and set it equal to zero. Constant terms > will have first derivatives that are zero, no matter > what the value of the constant. In your example, > sigma represents the standard deviation in the > population. It is simply a number, which is assumed > to be known. For purposes of estimating the > coefficients in a regression equation, it is > irrelevant. It can be disregarded. One might as well > drop it, at the potential cost of confusing some > students. David Greenbe > rg, Sociology Department, New York University > > ----- Original Message ----- > From: leechtcn <[email protected]> > Date: Thursday, October 30, 2003 6:42 am > Subject: st: Log Likelihood for Linear Regression > Models > > > Dear Listers, > > > > I have asked this question before. I am posting it > a > > second time in case you guys have not received it. > > > > I am sorry for the all convinence caused! > > > > I have a question concerning William Gould and > William > > Sribney's "MAximium Likelihood Estimation" (1st > > edition): > > > > > > In its 29th page, the author write the the > following > > lines: > > > > For instance, most people would write the log > > likelihood for the linear regression model as: > > > > LnL = > SUM(Ln(Normden((yi-xi*beta)/sigma)))-ln(sigma) > > (1) > > > > But in most econometrics textbooks, such as > William > > Green, the log likelihood for a linear regression > is > > only: > > > > LnL = SUM(Ln(Normden((yi-xi*beta)/sigma))) > > > (2) > > > > > > that is, the last item is dropped > > > > I have also tried to use (2) in stata, it will > give > > "no concave" error message. In my Monte Carlo > > experiments, (1) always gives reasonable results. > > > > Can somebody tell me why there is a difference > between > > stata's log likelihood and those of the other > > textbooks? > > > > thanks a lot > > > > Leecht > > > > > > > > __________________________________ > > Do you Yahoo!? > > Yahoo! SiteBuilder - Free, easy-to-use web site > design software > > http://sitebuilder.yahoo.com > > * > > * For searches and help try: > > * > http://www.stata.com/support/faqs/res/findit.html > > * http://www.stata.com/support/statalist/faq > > * http://www.ats.ucla.edu/stat/stata/ > > > > * > * For searches and help try: > * > http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ __________________________________ Do you Yahoo!? Yahoo! SiteBuilder - Free, easy-to-use web site design software http://sitebuilder.yahoo.com * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: Log Likelihood for Linear Regression Models***From:*David Greenberg <[email protected]>

- Prev by Date:
**Re: st: Log Likelihood for Linear Regression Models** - Next by Date:
**st: SV: Generating subsamples according to a binary choice** - Previous by thread:
**Re: st: Log Likelihood for Linear Regression Models** - Next by thread:
**Re: st: Log Likelihood for Linear Regression Models** - Index(es):

© Copyright 1996–2024 StataCorp LLC | Terms of use | Privacy | Contact us | What's new | Site index |