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Re: st: Log Likelihood for Linear Regression Models

From   David Greenberg <[email protected]>
To   [email protected]
Subject   Re: st: Log Likelihood for Linear Regression Models
Date   Fri, 31 Oct 2003 13:58:10 -0500

I agree. If a term involves parameters to be estimated, then it should not be dropped, but retained. Sometimes statistics books present artificial circumstances to make a point. For example, introductory texts often present a t test for a single mean based on the assumption that the standard deviation of the variable in the population is known. Often, if one knows the population standard deviation, one would also have the original data and could compute the population mean directly, rather than having to estimate it from a simple random sample. Incidentally, I just checked my copy of the 4th edition of Greene's ECONOMETRIC ANALYSIS, and on page 247 he gives the log-likelihood for a regression equation that includes the term in sigma squared.  - David Greenberg, Sociology Department, NYU

> In Leecht's example, the second term (aka B) is -ln(sigma), and
> sigma is a parameter to be estimated (not a known number!). So I
> don't see how one can drop this term in the estimation.

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