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From |
Chris Ruebeck <ruebeckc@lafayette.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: modeling constraint question |

Date |
Wed, 27 Apr 2005 10:30:53 -0400 |

I'm not sure if I understand your question, but let me try a couple of questions for you.

Do you want to constrain your function that estimates the parameters (what Nick is calling upstream)? Or constrain your predictions (downstream)? Constraining the predictions would seem to just be a matter of -replace-ing the predicted funding07 with the 10% limits you mention on funding06, perhaps using the lag operator if you want to condition the limits on a previous year's observations.

replace funding07 = (L.funding06)*1.1 if funding07>(L.funding06)*1.1

replace funding07 = (L.funding06)*0.9 if funding07<(L.funding06)*0.9

I would guess, though, that constraining the estimation is a better approach---correctly modeling the data generating process. To constrain the estimation, see the online and Reference Manual help for -constrain-.

Chris

On Apr 26, 2005, at 6:02 PM, William P. Kittredge wrote:

Does anyone know a way to constrain the predict function? I'm working with a simple funding model that the organization is comfortable with:

funding06 = bx1 + bx2+ bx3+e

predict funding07

I'd like to constrain the funding07 values for each grantee so that they are +/- a given percent (e.g. if the budget goes down 10% each grantee gets 10% less.

The reason for doing it this way, I'm told, is that the variables (e.g. x1) change differently from year to year for each grantee and that the 'need' they represent is also supposed to alter their funding (need based) all else equal.

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**References**:**st: modeling constraint question***From:*"William P. Kittredge" <kittredgewp@yahoo.com>

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