Austin Nichols replied to Cristina Solera:
> Under the most generous assumptions, you can only identify the
> "effect" of partner's occupational score for those with partners, i.e.
> you can get a coefficient on the interaction married*partneroccscore
> but not on the main effect partneroccscore, letting the interaction be
> zero whenever someone has no partner (assuming haspartner==married in
> this context). If you include both variables, the "marginal effect"
> of moving from married==0 to married==1 will be much trickier to
> estimate. You might prefer a set of indicators {notmarr, poorhusband,
> richhusband}, or somesuch (with the excluded category the middle third
> of partners' occupational scores, perhaps).
[...]
I know nothing of discrete time-duration models, but I do know that if
you're going to fit an interactive version of such a model, the
interaction _and_ both of its composite variables must be included so as
not to improperly constrain the latter variables to be zero (which could
well lead to the remaining parameter estimates being biased), thus:
Y = B0 + B1_X + B2_Z + B3_XZ + e.
The -haspartner- (B1) variable could only be interpreted when -occscore-
(B2) variable equals zero. Cristina never defined the scoring for this
variable, but if an interaction (B3) of the two variables is sensible
given her data, model and theory, and "occupational score" is never 0,
then B1, even if it _can_ be estimated, is meaningless anyway.
Estimating the marginal effect of B1 would simply be achieved by
calculating B1 + B3. Calculating its standard error is a bit trickier, but
it would be given by:
ME_B1 = sqrt((var_B1) + Z^2(B3) + 2Zcov(B1B3))
but, of course, this needs access to the variance-covariance matrix, and
only Cristina has that! See the link here
http://homepages.nyu.edu/~mrg217/interaction.html#literature
by Brambor, Clark and Golder for more on this, which also contains useful
Stata code to automate the calculation of MEs.
In essence, the main action is in the interaction: but the model has to be
properly estimated and its underlying theroretical expectations sound.
CLIVE NICHOLAS |t: 0(044)7903 397793
Politics |e: clive.nicholas@ncl.ac.uk
Newcastle University |http://www.ncl.ac.uk/geps
Whereever you go and whatever you do, just remember this. No matter how
many like you, admire you, love you or adore you, the number of people
turning up to your funeral will be largely determined by local weather
conditions.
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/