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From |
"Daniel Schneider" <[email protected]> |

To |
<[email protected]> |

Subject |
RE: st: Nonlinear regression and constraints |

Date |
Tue, 28 Jun 2005 23:49:18 -0700 |

```
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of
> Richard Williams
> Sent: Tuesday, June 28, 2005 11:23 PM
> To: [email protected]
> Subject: RE: st: Nonlinear regression and constraints
>
>
> At 10:43 PM 6/28/2005 -0700, Daniel Schneider wrote:
> >That might be possible. Let me clarify what I want to do: I want to
> >find the best (i.e. with the lowest SS) parameter values which are
> >between 0 and 1 (including both), because by definition they
> can only
> >be between those two values.
>
> I wonder what happens to your theory/definitions if both
> estimated values
> are significantly greater than 1! (Has a miracle occurred?)
> But suppose
> they are - can you then just impose the constraint that both
> parameters
> equal 1? Maybe there is some cheating here, but if a
> parameter is out of
> range you could constrain it to be in range.
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).
The problem is that I cannot constrain the parameter at all in -nl- (or,
at least I don't know how). I tried -cnsreg- as an alternative, but in
that case I need an additional constraint which turns my model into a
nonlinear model (because it really is nonlinear, and forcing it to be
not non-linear would probably just produce results that are even worse).
> > > Also, my impression is that -nl- doesn't need the
> constraints option
> > > because constraints can be specified using the -nl-
> command itself.
> >
> >Can you tell me how that can be done?
>
> The Stata 9 Reference Manual entry for -nl- has an example on pp.
> 297-298. Simplifying it a bit, the following will cause the
> effects of the
> 3 RHS vars to be equal:
>
> . sysuse auto
> . nl (mpg = {b0} + {b1}*price + {b1}*weight + {b1}*displacement)
>
> The reference manual basically says that this can save you
> some typing over
> doing the same thing in cnsreg. But, I also notice that -nl-
> gives you an
> R^2 stat, which was the request made in another thread today.
Yes it does (in my case this is a nice feature, because I can extend my
model and see if additional non-linearity can produce even better
results).
The approach you showed is basically something I already did in my
equation (this is slightly different than in my previous email, but the
concept remains the same):
nl (diff_diff = {alpha}*X1MX1SQDIVX3 - {beta}*X2MX2SQDIVX3 +
({alpha}+{beta})*X1X2DIVX3)
As you can see, I used alpha and beta twice. I could have used gamma
instead of alpha+beta and then tried to impose a restriction that it
should be equal to alpha+beta (that is what I am doing in -cnsreg-).
I just got a new idea, I might try a two-step solution, I can
reformulate my equation and start estimating one parameter and then
using the result to estimate a second one... I'll give that a try.
Thanks!
Daniel Schneider
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```

**Follow-Ups**:**RE: st: Nonlinear regression and constraints***From:*"Clive Nicholas" <[email protected]>

**Re: st: Nonlinear regression and constraints***From:*"Erik �. S�rensen" <[email protected]>

**RE: st: Nonlinear regression and constraints***From:*Richard Williams <[email protected]>

**References**:**RE: st: Nonlinear regression and constraints***From:*Richard Williams <[email protected]>

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