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From |
David Hoaglin <dchoaglin@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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 <ashcraftd@rocketmail.com> 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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: multicollinearity in regression coeffecient***From:*David Ashcraft <ashcraftd@rocketmail.com>

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