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From |
Richard Williams <Richard.A.Williams.5@nd.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: constrained least squares, multiple equations |

Date |
Thu, 04 Mar 2004 22:42:41 -0500 |

At 07:49 PM 3/4/2004 -0600, Scott Merryman wrote:

I don't believe this is the way to go -- in order to perform cross-equationI saw that too. But, since Olena wants to use least squares, the implication seems to be that the covariance between equations should be zero. But like I said originally, maybe 3sls would be more appropriate. We probably need to know more about the substance of the problem here.

tests you would need correlated errors.

From the help on -reg3, ols-

"Note that the covariance of the coefficients between equations is not estimated

under

this option and that cross-equation tests should not be performed after

estimation with

ols. For cross-equation testing, use sureg or 3sls (the default)."

Another possible way is pool the data, estimate a single fully interacted modelIf Y1 and Y2 and X1 and X2 stand for the values of Y and X in 2 different populations, then it is fairly straightforward to do this. But, if Y1, Y2, X1 and X2 are 4 different variables in the same population, it gets trickier. I think you'd have to duplicate the data and do various manipulations of the variables. Before going to the trouble of figuring all that out, it would be good to have a better understanding of the problem first.

and test whether or not the coefficient of the interaction term is equal to

zero. However, this does impose the assumption of homoskedasticity across the

model.

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**References**:**Re: st: constrained least squares, multiple equations***From:*Richard Williams <Richard.A.Williams.5@nd.edu>

**Re: st: constrained least squares, multiple equations***From:*"Scott Merryman" <smerryman@kc.rr.com>

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