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l52 - Models for Group Comparisons Summary Since we are...

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Models for Group Comparisons Summary Page 1 Models for Group Comparisons Summary Since we are estimating and comparing several models, it will be helpful to list several of them all in one place. This handout summarizes how to do group comparisons both by running separate models for each group and by using interaction terms. We won’t go through this handout separately, but it may help you to keep everything straight. I. Pooled (Constrained) Model. Blacks and whites are combined into a single analysis, hence the coefficient estimates are constrained to be the same for both racial groups, i.e. the intercepts and the effect of education and job experience are the same for both groups. . use "http://www.nd.edu/~rwilliam/stats2/statafiles/blwh.dta" . reg income educ jobexp Source | SS df MS Number of obs = 500 -------------+------------------------------ F( 2, 497) = 1103.96 Model | 32798.4018 2 16399.2009 Prob > F = 0.0000 Residual | 7382.84742 497 14.8548238 R-squared = 0.8163 -------------+------------------------------ Adj R-squared = 0.8155 Total | 40181.2493 499 80.5235456 Root MSE = 3.8542 ------------------------------------------------------------------------------ income | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | 1.94512 .0436998 44.51 0.000 1.859261 2.03098 jobexp | .7082212 .0343672 20.61 0.000 .6406983 .775744 _cons | -7.382935 .8027781 -9.20 0.000 -8.960192 -5.805678 ------------------------------------------------------------------------------ Hence, SSE c = 7382.84680, R 2 c = .81626, N = 500, K = 2, DFE c = 497. II. Unconstrained Models: Separate Models for each group. Here, we estimate separate models, first for whites, then blacks. This makes it possible for the intercepts and slope coefficients to freely differ across populations. This is equivalent to Model IIIC.
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