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From |
Kit Baum <baum@bc.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Re: LLF for linear regression |

Date |
Thu, 24 Jul 2003 09:34:01 -0400 |

On Thursday, Jul 24, 2003, at 02:33 US/Eastern, Chuntao wrote:

In page 29 of Maximum Likelihood Estimation with Stata (Gould andGoogle returns

Sribney 1999), the likelihood function for the linear regression model is

written as:

lnL=SUM (ln (phi((y-x*beta)/sigma)) - ln(sigma))

where phi() is the standard normal PDF.

My Question is: How the last term, ln(sigma), comes to the likelihood

function?

www.economics.unimelb.edu.au/subject_pages/ 2003/semester1/316-470/2regression.pdf

Take a look at the expression for loglikelihood, recalling that sigma is one of the parameters to be estimated in the regression problem.

Kit

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