Thanks.
If I set the coefficient of p1 equal to zero, then I only have three parameters
left in the model. Suppose e is the residual matrix for this regression, 2 by 2
here. Is the covariance matrix for the residuals, 2 by 2, still estimated by
t(e)%*%e/(n-3), where n is the number of observations?
Also, I want to specify different weights for each of the two equations. For
example, the first regression weighted by p1, and the second by R1. How can I
do that using systemfit? The systemfit("SUR") seems to deal with this problem,
but it does not allow one to set the weights explicitely. I wonder if you would
help me out on that.
Thanks a lot. Really appreiciate.
Sincerely,
Yanwei Zhang
Department of Actuarial Research and Modeling
Munich Re America
Tel: 609-275-2176
Email: [EMAIL PROTECTED]
-----Original Message-----
From: Patrizio Frederic [mailto:[EMAIL PROTECTED]
Sent: Friday, August 08, 2008 12:57 PM
To: Zhang Yanwei - Princeton-MRAm
Cc: [email protected]
Subject: Re: [R] Multivariate regression with constraints
Hi Zhang ,
take a look to sur package
http://www.systemfit.org/
regards,
Patrizio Frederic
+-------------------------------------------------
| Patrizio Frederic
| Research associate in Statistics,
| Department of Economics,
| University of Modena and Reggio Emilia, Via Berengario 51, 41100
| Modena, Italy
|
| tel: +39 059 205 6727
| fax: +39 059 205 6947
| mail: [EMAIL PROTECTED]
+-------------------------------------------------
2008/8/8 Zhang Yanwei - Princeton-MRAm <[EMAIL PROTECTED]>:
> Hi all,
> I am running a bivariate regression with the following:
>
> p1=c(184,155,676,67,922,22,76,24,39)
> p2=c(1845,1483,2287,367,1693,488,435,1782,745)
> I1=c(1530,1505,2505,204,2285,269,1271,298,2023)
> I2=c(8238,6247,6150,2748,4361,5549,2657,3533,5415)
> R1=I1-p1
> R2=I2-p2
>
> x1=cbind(p1,R1)
> y1=cbind(p2,R2)
>
> fit1=lm(y1~-1+x1)
> summary(fit1)
>
> Response 2:
> Coefficients:
> Estimate Std. Error t value Pr(>|t|)
> x1p1 -1.4969 2.7004 -0.554 0.59662
> x1R1 3.0937 0.8366 3.698 0.00767 **
>
>
> One can see that in the second regression, i.e. R2~-1+p1+R1, the coefficient
> for p1 is not significant. I wonder if I can run this bivariate regression
> again with the constraint that the coefficient for p1 in the second
> regression equation is zero? Thanks a lot.
>
> Sincerely,
> Yanwei Zhang
> Department of Actuarial Research and Modeling Munich Re America
> Tel: 609-275-2176
> Email: [EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]>
>
>
> [[alternative HTML version deleted]]
>
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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