Re: st: RES: generating a variable with pre-specified correlations with other two (given) variables

[fjc <fjc120@gmail.com>]

Thanks, Tirthankar.

This answers my question as originally posted.

Now, something I didn't say in my earlier post (and I think I should
have) is that after I generate the new variable (z) I would like to
run a regression of y on x and z. But if I generate z in the way you
propose, I will get perfect collinearity. ¿Is there any other way to
generate z without getting this collinearity?

Francisco.

P.D. The reason I want to run the aforementioned regression is the
following. Suppose I have an initial regression of y on x, and x turns
out to be insignificantly different from zero at some chosen
confidence level. Then I want to generate an example in which adding a
new (artificial) variable z as a covariate I can get x to become
significantly different from zero at the same confidence level. Based
on the formula for the t-test, I think I can do this if I can control
the correlations between the artificial variable and the original
ones. The excercise is just for expositional purposes, I do not want
to attach any deep meaning to it.


On Wed, Aug 31, 2011 at 9:00 AM, Tirthankar Chakravarty
<tirthankar.chakravarty@gmail.com> wrote:
> This question has appeared a few times before - in that you want to
> create a variable with a pattern of correlation with _existing_
> variables, which -corr2data- does not do. In an example where means
> are normalised to zero, this can be had by solving a system of linear
> equations in appropriate expectations.
>
> Suppose you generate a variable as
>
> Z = a*X+ b*Y ---(0)
>
> where a, and b are constants to be determined. Then you can derive the
> following identities under the zero mean assumption:
>
> Cov(Z, X) = a*Var(X) + b*Cov(X, Y)  ---(1)
> Cov(Z, Y) = b*Var(Y) + a*Cov(X, Y)  ---(2)
>
> Here you know everything (you set Cov(Z, X) and Cov(Z, Y)), and this
> is a system of two equations in two unknowns, a and b. Solve them and
> generate your variables as in equation (0).
>
> So for example, if I have Cov(X, Y) = .6, and Var(X)=Var(Y)=1, then a
> =0.15625 , b=0.40625.
> /************************************/
> mat mCov = (1, .6\ .6, 1)
> // generate x and y
> corr2data x y, cstorage(full) cov(mCov) n(100000) clear
> // generate z based on current sample of x and y
> g z = .15625*x+.40625*y
> corr, covariance
> /************************************/
>
> All these calculations are assuming zero means - more tedious algebra
> will allow you to generalise.
>
> T
>
> On Wed, Aug 31, 2011 at 3:53 AM, Henrique Neder <hdneder@ufu.br> wrote:
>> Try corr2data:
>>
>> matrix C = (1,0,.80,-.80\0,1,0,0\.80,0,1,-.80\-.80,0,-.80,1)
>> corr2data hsperc corzer1 corpos1 corneg1, n(4137) corr(C)
>>
>> Henrique Neder
>>
>>
>> -----Mensagem original-----
>> De: owner-statalist@hsphsun2.harvard.edu
>> [mailto:owner-statalist@hsphsun2.harvard.edu] Em nome de fjc
>> Enviada em: terça-feira, 30 de agosto de 2011 23:00
>> Para: statalist@hsphsun2.harvard.edu
>> Assunto: st: generating a variable with pre-specified correlations with
>> other two (given) variables
>>
>> Dear Statalisters:
>>
>> I have a dataset with two variables, x and y.
>>
>> I would like to generate a new artificial variable, z, with
>> pre-specified correlations with x and y (no particular distribution
>> required).
>>
>> Any help would be greatly appreciated.
>>
>> Best,
>>
>> Francisco.
>>
>> P.D. I'm using Stata 11 (on Windows XP)
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>
>
>
> --
> Tirthankar Chakravarty
> tchakravarty@ucsd.edu
> tirthankar.chakravarty@gmail.com
>
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