Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
<masa@uchicago.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Problems with plots from generalized sensitivity analysis (GSA) |

Date |
Sun, 27 May 2012 12:38:41 -0500 (CDT) |

This program -gsa- has not been uploaded on SSC, but I answer here because the question is posted to statalist, I will submit the program in a few month and the future users can refer to this post in the near future. -gsa- is currently available at my personal website with a few known minor bugs. -------------------------- Hi Bertel, I looked at your data and command, and tried to produce a good contour by myself. In this case, you are right. The small number of observation is the sole reason why you do not get a good contour. I felt that you understand what each option does correctly and indeed set precision at the small value (1). This does reduce the variations of the plots due to the error |t-\tilde{t}|, but does NOT reduce those due to the uncertainty of the contour, from which your analysis suffer. As the coefficient has both point estimate and standard errors, the contour has point estimate (in line) and variances. This is because of the fact that, with actual data, the size of omitted variable bias can be different from the value calculated from the canonical formula of OVB depending on the control variables. Although the correlation between pseudo unobservable and control variables is near zero, the correlation conditional on the treatment variable may not. First thing you might want to do is to drop control variables that are only weekly correlated with the treatment and outcome variables. Indeed, -gsa- produced much better contour with your data with a few control variables. Another thing is to use multiple imputation and to use the most conservative contour to defend your claim if your data have many incomplete cases. If you still have the problem, how should you interpret the widely-dispersed scatter plots and the contour with wide margin? Because you usually do not know the sign and strength of the correlation between pseudo unobservable and control variables conditional on the treatment variable, you might want to interpret such contour like confidence interval. That is, first drop 5% of the contour that are closest ("in terms of what?" would be another question which I have not figured out), and use the line that connects the plots closest to the origin as a lower-bound contour. In this way, you can trust the contour with 95% CI. -- MASATAKA HARADA masa@uchicago.edu http://home.uchicago.edu/~masa/top.html 1-312-952-6124 * * 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/

**References**:**st: Problems with plots from generalized sensitivity analysis (GSA)***From:*Bertel Teilfeldt Hansen <statalistbertel@gmail.com>

- Prev by Date:
**Re: st: Passing value of an observation to a user-written function fails** - Next by Date:
**st: instrumenting interaction and Hansen J statistic** - Previous by thread:
**st: Problems with plots from generalized sensitivity analysis (GSA)** - Next by thread:
**st: Extracting substrings from variables.** - Index(es):