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From |
Richard Williams <[email protected]> |

To |
[email protected] |

Subject |
Re: st: generating random number with restriction to add to a variable in current dataset |

Date |
Wed, 23 Feb 2005 17:31:11 -0500 |

At 05:45 PM 2/23/2005 -0300, you wrote:

I'm not clear how random numbers could do this. In order for xb+v>0| y=1, the lower bound of v has to be -xb. I suppose you could set v to the max of -xb and some random number, but then you are not really adding random numbers anymore. Or perhaps you shift your random variable so its lower bound is -xb (if y = 1) or its upper bound is -xb (if y = 0). Something like this after a probit would do that (this may need a little tweaking):Dear all, I want to generate random numbers to add to the linear index of a probit model (xb) so that I can predict precisely the 1/0 observed outcomes. Therefore, for y=1 I want to draw a random number (vi)from a N~(0,1) so that xb+v>0 and for y=0 I want to draw a random number (vi) so that xb+v<=0. Any idea on how I can draw these numbers and add them to the index in few steps? Best wishes Fabio

predict xb, xb

drawnorm resid0 resid1

egen residmin1 = min(resid1) if y==1

gen newresid = resid1 - residmin1 - xb + .001 if y==1

egen residmax0 = max(resid0) if y==0

replace newresid = resid0 - residmax0 - xb - .001 if y==0

gen newxb = xb + newresid

bysort y: sum xb newxb

The newresid won't be normal (0,1) though, the mean will be shifted so as to meet the necessary upper/lower bound conditions.

There may be better solutions, but whatever you do I don't think you can just say the numbers are drawn from a N(0,1) because there have to be constraints on the residuals to achieve what you want.

-------------------------------------------

Richard Williams, Notre Dame Dept of Sociology

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