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Re: st: multicollinearity in regression coeffecient
From
David Hoaglin <[email protected]>
To
[email protected]
Subject
Re: st: multicollinearity in regression coeffecient
Date
Mon, 24 Dec 2012 09:20:10 -0500
David,
It appears from the output that all 85 observations with id==1 have
the same value of x (that may be what you meant when you said, "X is
fixed"). What do you see when you make a scatterplot of y versus x?
(It is a good idea to plot the data before trying to fit a
regression.)
If all the values of x are the same, then x is collinear with the
constant in the regression line. A regression line summarizes the
relation between change in y and change in x. If x is constant, the
data provide no information on change in x.
David Hoaglin
On Mon, Dec 24, 2012 at 2:33 AM, David Ashcraft
<[email protected]> wrote:
> Hi Statalist:
>
> I have following regression: y=a0+b1X. The data is time series
> y variable takes on different values, however X is fixed. now when I run my simple regression and I get the following results:
>
> regress y x if id==1
> note: x omitted because of collinearity
>
> Source | SS df MS Number of obs = 85
> -------------+------------------------------ F( 0, 84) = 0.00
> Model | 0 0 . Prob > F = .
> Residual | .195687827 84 .002329617 R-squared = 0.0000
> -------------+------------------------------ Adj R-squared = 0.0000
> Total | .195687827 84 .002329617 Root MSE = .04827
>
> ------------------------------------------------------------------------------
> y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> x | (omitted)
> _cons | .0017973 .0052352 0.34 0.732 -.0086134 .0122081
> ------------------------------------------------------------------------------
>
> I checked the collinearity via -collin- and results are below:
>
> collin y x if id==1
> (obs=85)
> corr(): matrix has zero or negative values on diagonal
> r(504);
>
> The correlation between y and x is:
>
> corr y x if id==1
> (obs=85)
>
> | y x
> -------------+------------------
> y | 1.0000
> x | . .
>
> I am confused and need some help here on how to resolve this issue. There are several papers reporting results based on the above regression. Any suggestion?
> Regards
>
> David
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