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From |
Kit Baum <[email protected]> |

To |
[email protected] |

Subject |
st: RE: Re: obtaining confidence intervals after mfx compute |

Date |
Tue, 31 Aug 2004 07:46:09 -0400 |

On Aug 31, 2004, at 2:33 AM, Jose wrote:

Thanks very much. The problem is that stdp generates the standard error forI don't get it. If you are using standard linear regression, you get a point and interval estimate on the coefficient of X. It doesn't matter what value X takes on (4 or 400) since the point and interval estimates do not depend on the value of X. So \partial Y / \partial X is \hat{\beta}, and \hat{beta} has a confidence interval, since it is a random variable. There is thus a confidence interval for the random variable \hat{Y}, given by multiplying dX by the upper and lower limits of the confidence interval for \hat{\beta}. That gives you a range of values on the Y scale that would arise by changing that particular X by dX.

the fitted values of X. This is also commonly referred to as the standard

error of the observation's covariate pattern xjb. In the panel data I'm

using, this error is unique to each observation fitted. The reason I use mfx

compute, however, is because I want to isolate the effect of a discrete

change in one variable on the dependent variable. If I x changes from 4 to

5, and y increases by 20, how do I get the standard error for this

particular change?. Unfortunately, I don't know how to get stata to get

stata to do this for E[y | x].

mfx compute, applied after regress, gives the CI for each \hat{\beta}, but in terms of Z, not t. It does not tell you anything that regress does not (apart from the mean values of the regressors). But what are you trying to compute from the regression?

Kit

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