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From |
Misha Spisok <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Difference in Difference for Proportions |

Date |
Tue, 22 Sep 2009 21:51:32 -0700 |

Thank you, Austin! I wasn't paying attention to the weight (i.e., pw vs. fw); I just copied verbatim without thinking (rarely a good idea...). This makes a lot more sense--and allows me to use -inteff-. Misha On Tue, Sep 22, 2009 at 5:25 PM, Austin Nichols <[email protected]> wrote: > Misha Spisok <[email protected]> : > If the variable f records the true number of observations with that > covariate pattern, then > logit union t south txsouth [fw=f] > would be the right code (see -help weight-). > > You can also use the -svy- commands I outlined, starting with the command > svyset, srs > or > svyset _n > to declare the data as not coming from a complex survey. > > On Tue, Sep 22, 2009 at 5:49 PM, Misha Spisok <[email protected]> wrote: >> Many Thanks, Austin and Jeph! >> >> The Norton, Wang, and Ai SJ article was very informative. Also, the >> code examples clarified some things and, of course, raised more >> questions. >> >> If it is not kosher to post follow-up questions on the same thread, >> please let me know and I will re-post as new questions. Otherwise, my >> follow-up questions are below. >> >> The short version is, what's the difference between -blogit- and >> -logit-? Or, more accurately, in the context of grouped data, which >> standard error estimate is correct? >> >> If, after using Austin's example, I run the following: >> >> logit union t south txsouth [pw=f] >> >> and >> >> blogit y pop t south txsouth >> >> I get, as expected (or hoped, in my case), the same coefficients. The >> standard errors are smaller in -blogit- because, as I might >> understand, -blogit- is considering pop to be the number of >> observations per row, so the number of "effective" observations is the >> sum of pop. >> >> I think this explains the difference in the standard errors. >> Specifically, with some minor adjustment for the "robustified" -logit- >> standard errors, the relationship between -logit- and -blogit- >> standard errors is something like the following: >> >> s_blogit = sqrt(s_logit^2*(n_logit - k)/(n_blogit - k)) >> >> where s_blogit is the se from -blogit-, s_logit is the se from >> -logit-, n_logit is the number of observation from -logit-, n_blogit >> is the number of observations from -blogit-, and k is the number of >> dependent variables, including the constant. >> >> It strikes me that the standard errors from -blogit- are more >> reasonable, given the actual number of observations that lie behind >> the summarized data. Thus, it seems that the standard errors from >> using -inteff- will be as incorrect as those from -logit- for >> summarized data. While I could use the formula from Ai and Norton >> (2003) to calculate the standard error for the interaction term using >> the variance-covariance matrix returned after -blogit-, would this be >> making a mistake? >> >> My data are not survey data. They are "actual" data, in the sense >> that f is the true number of people with the condition and pop is the >> true population. >> >> Thanks again, >> >> Misha >> (Using Stata 10.1) >> >> >> On Fri, Sep 18, 2009 at 7:00 AM, Jeph Herrin <[email protected]> wrote: >>> >>> Thanks Austin, >>> >>> Yes, I should have specified the -rd- option, I meant >>> the linear link function. I've become a fan of using >>> binary (and binomial) linear regression for testing >>> hypotheses. >>> >>> cheers, >>> Jeph >>> >>> >>> Austin Nichols wrote: >>>> >>>> Jeph-- >>>> Doesn't the interaction problem discussed in >>>> http://www.stata-journal.com/sjpdf.html?articlenum=st0063 >>>> also rear its ugly head here? >>>> >>>> Probably also have to be careful of SEs--if the total populations are >>>> summed weights from a survey, significance will likely be overstated. >>>> >>>> I'd probably go to -svy:tab- first in that case... >>>> >>>> sysuse psidextract, clear >>>> keep if t>5 >>>> set seed 1 >>>> g f=ceil(uniform()*1000) >>>> egen pop=total(f), by(south t) >>>> svyset [pw=f], strata(t) >>>> egen gp=group(t south), lab >>>> svy:tab gp union if t>5, row ci >>>> lincom _b[p42]-_b[p22]-(_b[p32]-_b[p12]) >>>> g txsouth=t*south >>>> egen y=total(union*f), by(gp) >>>> bys gp: replace y=. if _n<_N >>>> li y t south pop if y<. >>>> binreg y t south txsouth, n(pop) >>>> binreg union t south txsouth [pw=f] >>>> logit union t south txsouth [pw=f] >>>> findit inteff >>>> >>>> On Thu, Sep 17, 2009 at 4:53 PM, Jeph Herrin <[email protected]> wrote: >>>>> >>>>> Not sure whether this helps you, but I would normally test this >>>>> with an interaction term in a model. For instance >>>>> >>>>> gen txsouth=t*south >>>>> binreg f t south txsouth, n(pop) >>>>> >>>>> Then testing the coefficient on -txsouth- is the same as >>>>> testing whether there is a significant difference in differences. >>>>> >>>>> hth, >>>>> Jeph >>>>> >>>>> Misha Spisok wrote: >>>>>> >>>>>> Hello, Statalist, >>>>>> >>>>>> In brief, how does one test a difference in difference of proportions? >>>>>> My question is re-stated briefly at the end with reference to the >>>>>> variables I present. A formula and/or reference would be appreciated >>>>>> if no command exists. >>>>>> >>>> >>>> > > * > * 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/

**References**:**st: Difference in Difference for Proportions***From:*Misha Spisok <[email protected]>

**Re: st: Difference in Difference for Proportions***From:*Jeph Herrin <[email protected]>

**Re: st: Difference in Difference for Proportions***From:*Austin Nichols <[email protected]>

**Re: st: Difference in Difference for Proportions***From:*Jeph Herrin <[email protected]>

**Re: st: Difference in Difference for Proportions***From:*Misha Spisok <[email protected]>

**Re: st: Difference in Difference for Proportions***From:*Austin Nichols <[email protected]>

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