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Re: st: time-invariant regressors in xtdpdsys


From   "David M. Drukker" <[email protected]>
To   Stata List <[email protected]>
Subject   Re: st: time-invariant regressors in xtdpdsys
Date   Wed, 6 Aug 2008 14:14:49 -0500 (CDT)

Hewan Belay <[email protected]> asked how to prevent -xtdpd- to from
dropping time-invariant regressors.

The answer is the -hascons- option.

Hewan asked this question in the context of how to get -xtdpd- to reproduce
the results of -xtdpdsys-.

Below my signature is a version of Hewan's example that answers these
questions.

   --David
     <[email protected]>

. webuse abdata, clear

. by id, s: egen avg_emp = mean(emp)

. /* No. 1 */    xtdpdsys         n w k avg
note: avg_emp dropped from div() because of collinearity

System dynamic panel-data estimation         Number of obs         =       891
Group variable: id                           Number of groups      =       140
Time variable: year
                                             Obs per group:    min =         6
                                                               avg =  6.364286
                                                               max =         8

Number of instruments =     38               Wald chi2(4)          =   4550.53
                                             Prob > chi2           =    0.0000
One-step results
------------------------------------------------------------------------------
           n |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           n |
         L1. |   .5514304    .043601    12.65   0.000      .465974    .6368869
           w |  -.6130319   .0513926   -11.93   0.000    -.7137595   -.5123042
           k |   .3334923   .0239851    13.90   0.000     .2864823    .3805022
     avg_emp |  -.0032794   .0042283    -0.78   0.438    -.0115668    .0050079
       _cons |   2.541278   .1785823    14.23   0.000     2.191263    2.891293
------------------------------------------------------------------------------
Instruments for differenced equation
        GMM-type: L(2/.).n
        Standard: D.w D.k
Instruments for level equation
        GMM-type: LD.n
        Standard: _cons

. /* No. 2 */    xtdpd L(0/1).n w k avg, div(w k) dgmmiv(n) lgmmiv(n) hascons

Dynamic panel-data estimation                Number of obs         =       891
Group variable: id                           Number of groups      =       140
Time variable: year
                                             Obs per group:    min =         6
                                                               avg =  6.364286
                                                               max =         8

Number of instruments =     38               Wald chi2(4)          =   4550.53
                                             Prob > chi2           =    0.0000
One-step results
------------------------------------------------------------------------------
           n |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           n |
         L1. |   .5514304    .043601    12.65   0.000      .465974    .6368869
           w |  -.6130319   .0513926   -11.93   0.000    -.7137595   -.5123042
           k |   .3334923   .0239851    13.90   0.000     .2864823    .3805022
     avg_emp |  -.0032794   .0042283    -0.78   0.438    -.0115668    .0050079
       _cons |   2.541278   .1785823    14.23   0.000     2.191263    2.891293
------------------------------------------------------------------------------
Instruments for differenced equation
        GMM-type: L(2/.).n
        Standard: D.w D.k
Instruments for level equation
        GMM-type: LD.n
        Standard: _cons


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