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st: multicollinearity in regression coeffecient


From   David Ashcraft <ashcraftd@rocketmail.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
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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