stata - include panel-specific trends in a first-difference regression -


i wondering if there's way include panel-specific or varying trends in first-difference regression when clustering on panel id , time variable.

here's example of stata:

. webuse nlswork (national longitudinal survey.  young women 14-26 years of age in 1968)  . ivreg2 s1.(ln_wage tenure) , cluster(idcode year)  ols estimation --------------  estimates efficient homoskedasticity statistics robust heteroskedasticity , clustering on idcode , year  number of clusters (idcode) =     3660                number of obs =    10528 number of clusters (year) =          8                f(  1,     7) =     2.81                                                       prob > f      =   0.1378 total (centered) ss     =  1004.098948                centered r2   =   0.0007 total (uncentered) ss   =  1035.845686                uncentered r2 =   0.0314 residual ss             =   1003.36326                root mse      =    .3087  ------------------------------------------------------------------------------              |               robust    s.ln_wage |      coef.   std. err.      z    p>|z|     [95% conf. interval] -------------+----------------------------------------------------------------       tenure |          s1. |   .0076418   .0042666     1.79   0.073    -.0007206    .0160043              |        _cons |   .0501738   .0070986     7.07   0.000     .0362608    .0640868 ------------------------------------------------------------------------------ included instruments: s.tenure ------------------------------------------------------------------------------  . ivreg2 s1.(ln_wage tenure i.c_city), cluster(idcode year) factor variables not allowed r(101);

in specification above, constant corresponds common time trend. putting factor variable outside seasonal difference operator errors well.

i understand differencing operator not play factor variables or interactions, feel there must hack around that.

the ivreg2 bit of red herring. not doing iv estimation, want use two-way clustering.

you same solution @metrics if xi: ivreg2 s1.(ln_wage tenure) i.ind_code , cluster(idcode year)


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