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RE: st: Nonlinear regression and constraints


From   "Clive Nicholas" <Clive.Nicholas@newcastle.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   RE: st: Nonlinear regression and constraints
Date   Wed, 29 Jun 2005 11:42:13 +0100 (BST)

Daniel Schnieder wrote:

> Without going to much into detail: my parameters are percentages. They
> can only range from 0 to 1. There may be a better solution (i.e. a
> solution that better fits the data) beyond 1, BUT, as I said, by
> definition they cannot be above 1 (or below 0). So the best solution
> that is possible has to be between 0 and 1.
> My current model gives me values which are both below 0 (I changed the
> equation a little bit, corrected a minor error, but that doesn't matter
> for the problem).

Since the response variables in your models are 0-1 proportions, you may
find the -mlbeta- routine useful. Maximum-likelihood beta-distributed
regressions have been shown to outperform other models in terms of
producing more accurate and precise results (Paolino 2001). Alas, it
cannot do constraints (I'm busy trying to knit my own, with some success
from members of the list).

This program is _not_ downloadable from SSC. To download, go:

. net from http://www2.bc.edu/~bucklesj

and then click on -mlbeta-. Have fun!

----------------------------------------------------------------------------
help for mlbeta
----------------------------------------------------------------------------

Maximum likelihood estimation with Beta-distributed dependent variables

mlbeta depvar [varlist] [if exp] [in range] [, di:spersion(varlist) ]
             [, robust] [svy]



Description

mlbeta is an implementation of Paolino's (2001) method of estimating the
effect on a beta-distributed depvar of varlist.


Options

di:spersion(varlist) allows for the estimation of an auxiliary dispersion
    equation.


Examples

. mlbeta proportion var1 var2, di(var2 var3)       (estimate model)

Reference
Paolino, Philip. 2001.  "Maximum Likelihood Estimation of Models with
Beta-distributed Dependent Variables." Political Analysis Vol. 9, No. 4,
Autumn 2001.

Author
Jack Buckley, Department of Political Science SUNY, Stony Brook.
sbuckley@ic.sunysb.edu

CLIVE NICHOLAS        |t: 0(044)7903 397793
Politics              |e: clive.nicholas@ncl.ac.uk
Newcastle University  |http://www.ncl.ac.uk/geps

Whereever you go and whatever you do, just remember this. No matter how
many like you, admire you, love you or adore you, the number of people
turning up to your funeral will be largely determined by local weather
conditions.

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