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From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: constrained linear least-squares problems without using ML |

Date |
Wed, 29 Jun 2011 10:27:33 +0200 |

On Wed, Jun 29, 2011 at 3:51 AM, ali hashemi wrote: > I would like to estimate an OLS model (y=b1*x1+b2*x2) with proportionate > coefficients which means considering the following constraints: > b1>0 > b2>0 > b1+b2=1 > > I tried to estimate this using ML (for more details: findit inequality > constraints) > > It works for some cases. Unfortunately, for many other cases it keeps giving > this message: "flat or discontinuous region encountered" > > I'm told that ML is not the best option to estimate constrained linear > least-squares models. lsqlin in MATLAB and quadratic programming in R are > solutions that I have found in other packages. However, I'm not aware of any > alternative method in Stata? Does anyone have any idea how constrained > linear least-squares models can be estimated without using ML? If you go to <http://www.stata.com/support/faqs/stat/intconst.html> you will see that the first thing it says is: "Note: The examples in this tutorial are for illustration purposes only. If you need to fit a linear regression with interval constraints, use the Stata command nl." Now I expect you will still need to think very carefully about starting values. If -ml- gets into trouble like that than I expect that there is a good possibility that -nl- will also not converge without good starting values. Even with good starting values you may end up on a boundary solution, in which case it will not be easy to get convergence whichever method you use. Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: constrained linear least-squares problems without using ML***From:*"ali hashemi" <hashemi@vt.edu>

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