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From |
"Jing Zhou" <jing.zhou@rmit.edu.au> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: dummy variable |

Date |
Fri, 16 Sep 2011 20:06:22 +1000 |

Thanks a lot, Maarten. your suggestions are very appreciated. Can you please provide the reference of "The consequence of the fact that there are so few 1s is that the variance of the variable will be low, and thus the precision with which the effect of that variable is measured will also be low, i.e. large standard errors and confidence intervals."? Regards, Jing >>> Maarten Buis <maartenlbuis@gmail.com> 16/09/11 7:30 下午 >>> If you want to get results that control for that than you should add that variable, otherwise you should not do so. If you want to add that variable I would first try to find out why they are missing. For example, is there only one shareholder, and is it thus impossible for anyone to be the second largest shareholder or is the second largest shareholder unknown. In the former case you can set those missing values at zero (and possible add a dummy variable for single shareholder), while in the latter case you can think of multiple imputation (see: -help mi-). The consequence of the fact that there are so few 1s is that the variance of the variable will be low, and thus the precision with which the effect of that variable is measured will also be low, i.e. large standard errors and confidence intervals. Hope this helps, Maarten On Fri, Sep 16, 2011 at 10:51 AM, Jing Zhou <jing.zhou@rmit.edu.au> wrote: > Thanks Maarten, it measues whether the second largest shareholder of a > listed company is a state shareholder. > > Jing > > >>>> Maarten Buis <maartenlbuis@gmail.com> 16/09/11 6:02 下午 >>> > On Fri, Sep 16, 2011 at 2:09 AM, Jing Zhou wrote: >> I have a sample period from 2000-2009. now I am considering to add a > dummy variable into the regression. for the year of 2009, only 24 out of > 550 observations (<5%) valued at 1 of this dummy. for 2008, 31 out of > 502 observations (<5%) valued at 1. further, for the year of 2000-2002, > the missing value rate is 61.19%, 31.91%, and 13.33%, respectively. for > other variables of the regression, i can get relatively complete data > value of each observation. therefore, is it still necessary to include > this dummy in my regression? thanks! > > What is this dummy variable supposed to measure? > > -- Maarten > > -------------------------- > Maarten L. Buis > Institut fuer Soziologie > Universitaet Tuebingen > Wilhelmstrasse 36 > 72074 Tuebingen > Germany > > > http://www.maartenbuis.nl > -------------------------- > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > -- -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: dummy variable***From:*Maarten Buis <maartenlbuis@gmail.com>

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