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st: multicollinearity in regression coeffecient
From
David Ashcraft <[email protected]>
To
"[email protected]" <[email protected]>
Subject
st: multicollinearity in regression coeffecient
Date
Sun, 23 Dec 2012 23:33:40 -0800 (PST)
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
--
David Ashcraft
Bangor University
Bangor, UK
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