Do Han Kim wrote:
> I understand why coefficients of dummy variables change as I change
> the reference group. But, should slope coefficient (continuous
> variable) be consistent regardless of which reference group I include?
> For example, if I run the following two models(1) and(2), (w1fer and
> w1awer are continuous variables and w1pydu1-w1pydu6 are dummy
> variables. All are multiplied by weights), should w1awer show
> consistent coefficient? Otherwise, which output should I report as my
> result?
This problem must be peculiar to your own data, because it's not witnessed
here, using -wls0- again:
. webuse grunfeld
. tab time, gen(t)
. g weight=(1/invnorm(uniform()))^2
. wls0 invest mvalue kstock t1- t19, wvar(weight) type(abse)
WLS regression - type: proportional to abs(e)
(sum of wgt is 3.3734e+00)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 21, 178) = 37.84
Model | 7570651.26 21 360507.203 Prob > F = 0.0000
Residual | 1695667.55 178 9526.22222 R-squared = 0.8170
-------------+------------------------------ Adj R-squared = 0.7954
Total | 9266318.82 199 46564.4162 Root MSE = 97.602
----------------------------------------------------------------------------
invest | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------+----------------------------------------------------------------
mvalue | .1170477 .0063095 18.55 0.000 .1045967 .1294987
kstock | .2191208 .0322738 6.79 0.000 .1554322 .2828093
[...]
_cons | -35.87942 35.74122 -1.00 0.317 -106.4105 34.65162
----------------------------------------------------------------------------
. wls0 invest mvalue kstock t2- t20, wvar(weight) type(abse)
WLS regression - type: proportional to abs(e)
(sum of wgt is 3.3734e+00)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 21, 178) = 37.84
Model | 7570651.26 21 360507.203 Prob > F = 0.0000
Residual | 1695667.55 178 9526.22222 R-squared = 0.8170
-------------+------------------------------ Adj R-squared = 0.7954
Total | 9266318.82 199 46564.4162 Root MSE = 97.602
----------------------------------------------------------------------------
invest | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------+----------------------------------------------------------------
mvalue | .1170477 .0063095 18.55 0.000 .1045967 .1294987
kstock | .2191208 .0322738 6.79 0.000 .1554322 .2828093
[...]
_cons | -23.71396 31.27414 -0.76 0.449 -85.42974 38.00183
----------------------------------------------------------------------------
I don't witness this in my own data, either. The constant changes along
with the change of dummies. Try -wls0- as I suggested and see what
happens.
Another solution to is to constrain your dummy coefficients to 1 or 0 -
whichever is the most appropriate - and then running -reg- (or -wls0-)
with the -nocons- option.
CLIVE NICHOLAS |t: 0(044)7903 397793
Politics |e: [email protected]
Newcastle University |http://www.ncl.ac.uk/geps
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