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| 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 * * 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/