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From |
"G. Anderson" <ga274@cam.ac.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Nonlinear least squares restrictions |

Date |
06 Jun 2013 09:40:40 +0100 |

Hi Maarten,

Thanks for all your help, Gareth On Jun 5 2013, Maarten Buis wrote:

On Wed, Jun 5, 2013 at 4:34 PM, G. Anderson wrote:Is the right setup using propcnsreg: yield dum00 dum01 dum02 dum03 dum04 dum05 ,constrained(gdp unemployment inflation) lambda(dum00 dum01 dum02 dum03 dum04 dum05)Where dum are annual dummies. In particular- do I include the annualdummies as both independent variables and variables in lambda?Yes, I did it this way to allow for a more flexible specification of time as the main effect than in the interaction effect (-lambda()-). So, you could have time as dummies for the main effect, but constrain time to be linear in the interaction effect. The other way around is technically possible, but I would not recommend it.Is it also possible to impose a normalization so that on average the coefficients on the dummies is equal to 1?Just create your variables using an effect or sigma or deviation coding scheme (different names for the same thing). Hope this helps, Maarten --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Nonlinear least squares restrictions***From:*"G. Anderson" <ga274@cam.ac.uk>

**Re: st: Nonlinear least squares restrictions***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: Nonlinear least squares restrictions***From:*"G. Anderson" <ga274@cam.ac.uk>

**Re: st: Nonlinear least squares restrictions***From:*Maarten Buis <maartenlbuis@gmail.com>

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