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From | Maarten buis <maartenbuis@yahoo.co.uk> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: comparing coefficients and retrieve b from bootstrap |
Date | Sat, 13 Mar 2010 00:17:14 -0800 (PST) |
--- On Fri, 12/3/10, G. Dai wrote: > I'm interested in compare two coefficients from the following two > regressions: > y=a0+a1*X1+a2*X2+ey > z=b0+b1*X1+b2*X3+ez <snip> > Unfortunately, y is truncated in my data and z is a dummy and thus a > tobit and a probit will be a better choice. Keep this in mind, I have to > compare a1 and b1 form the regressions: > tobit y X1 X2 [pw=weight], ll(.) ul(.) > probit z X1 X3 [pw=weight] You can combine models using -suest-, see: -help suest-. However, why do you expect the two coefficients to be the same? The unit of the depedent variable is likely to be very different, so a direct comparison of the two coefficients boils down to comparing apples and oranges. You could turn the dependent variable of the -tobit- and -logit- regressions into a similar metric by first computing the standard deviations of the latent variables, and than divide the parameter of the variable of interest by this standard deviation The latent variable in both cases is y* = b0 + b1 x + e (assuming you have 1 explanatory variables x). Using standard rules for variances and standard deviations (see <http://en.wikipedia.org/wiki/Variance>, you can see that that the variance of y* is: b1^2*var(x) + var(e) var(e) is the sigma that is returned by -tobit- squared or 1 in -probit-, and var(x) can be obtained from -summarize-. Putting this together: *--------------------------- begin example ------------------------- sysuse auto, clear generate wgt=weight/1000 tobit mpg wgt, ll(17) est store a probit foreign wgt est store b suest a b sum wgt local sd_y "sqrt([a_model]_b[wgt]^2*`r(Var)' + [a_sigma]_b[_cons])" local sd_z "sqrt([b_foreign]_b[wgt]^2*`r(Var)' + 1)" testnl [a_model]_b[wgt] / `sd_y' = [b_foreign]_b[wgt] / `sd_z' *---------------------------- end example --------------------------- ( For more on how to use examples I sent to statalist see: http://www.maartenbuis.nl/stata/exampleFAQ.html ) If you have multiple explanatory variables you also have to take into account the covariances between the explanatory variables when computing the standard deviation of y*. The relevant formulas are shown on the wikipedia page I linked to earlier. Hope this helps, 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/