st: Constructing matrix regressors with thousands of dummy variables

[Ivan Png <iplpng@gmail.com>]

Dear Statalist

I am researching the effects of various factors on mobility of
inventors. The dependent variable, M_it is an indicator = 1 if
inventor i changed employer in year t, else = 0.  The explanatory
variables include marital status, education level, and citizenship.  I
also include inventor, state, and year fixed effects.  Originally, I
simply estimated a linear probability model by areg with
absorb(inventor) and explanatory variables comprising married,
bachelor, citizen, i.state and i.year.

However, the measure of mobility is subject to error.  Obviously, this
error cannot be classical.  For instance, if the observed M_it = 1 is
wrong, then the true M_it = 0.  I would like to apply the method of
Meyer and Mittag, U of Chicago (2012) to characterize the bias due to
the error.

For this, I need to calculate the conditional expectation of the
matrix of explanatory variables, X, conditional on error in
measurement of M_it.  I have two data-sets, one with measurement error
and one without, so, I can identify the observations with error.

Question:
How to construct the matrix, X, and the inverse matrix, (X'X)^-1?  The
online guides teach me how to construct a matrix when there are two or
three explanatory variables.

. mata
: st_view(y= , , "mobility")
: st_view(X= , , "married", "bachelor", "citizen")

But, in my case, I have dummies for 10,000 inventors plus the state
and year fixed effect.  The above method doesn't seem practical.

Can I run areg and retrieve the X and inverse matrix, (X'X)^-1?

May I please have your help?

 -
Best wishes
Ivan Png
Skype: ipng00
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