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I am using the parametric approach and non-parametric (local linear regression) approaches of regression discontinuity design (RDD) to compute the treatment effect using Stata.

To get the user-written rd and the 102nd Congress data, I do this:

net get rd
use votex

The local linear approach:

rd lne d,bw(0.20) mbw(100) ker(rec)
Two variables specified; treatment is 
assumed to jump from zero to one at Z=0. 

 Assignment variable Z is d
 Treatment variable X_T unspecified
 Outcome variable y is lne

Estimating for bandwidth .2
------------------------------------------------------------------------------
         lne |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       lwald |  -.1046939   .1147029    -0.91   0.361    -.3295075    .1201197
-----------------------------------------------------------------------------

As far as I understand this is equivalent to following :

gen win_d=win*d
reg lne d win win_d if d>=-0.2 & d<=0.2

      Source |       SS       df       MS              Number of obs =     267
-------------+------------------------------           F(  3,   263) =    0.43
       Model |  .271662326     3  .090554109           Prob > F      =  0.7339
    Residual |  55.7885045   263  .212123591           R-squared     =  0.0048
-------------+------------------------------           Adj R-squared = -0.0065
       Total |  56.0601668   266  .210752507           Root MSE      =  .46057

------------------------------------------------------------------------------
         lne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           d |   .8450601   .7855123     1.08   0.283    -.7016333    2.391753
         win |  -.1046939   .1257913    -0.83   0.406    -.3523801    .1429923
       win_d |  -.8707605   1.048807    -0.83   0.407    -2.935887    1.194366
       _cons |   21.44195   .0925378   231.71   0.000     21.25974    21.62415
------------------------------------------------------------------------------

However, when we use the parametric approach (let's say with the polynomial of order one), we use all the observations. But, I am trying to see how parametric approach can be compared with non-parametric approach with the same number of observation as in non-parametric approach. So, I do as follows:

reg lne d win if d>=-0.2 & d<=0.2

      Source |       SS       df       MS              Number of obs =     267
-------------+------------------------------           F(  2,   264) =    0.30
       Model |  .125446108     2  .062723054           Prob > F      =  0.7440
    Residual |  55.9347207   264  .211873942           R-squared     =  0.0022
-------------+------------------------------           Adj R-squared = -0.0053
       Total |  56.0601668   266  .210752507           Root MSE      =   .4603

------------------------------------------------------------------------------
         lne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           d |   .3566172   .5201877     0.69   0.494    -.6676274    1.380862
         win |  -.0964314   .1253232    -0.77   0.442    -.3431916    .1503288
       _cons |   21.39136   .0696112   307.30   0.000      21.2543    21.52843
------------------------------------------------------------------------------

My concern is why the non-parametric approach result (-.1046939) is not the same as parametric approach (-.0964314), although we are using the same observation for both.

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This is happening because you are restricting the effect of Democratic vote share to be the same on both sides of the cutoff in your third specification, which is a slightly different model. As the magnitude and significance of the interaction term in (2) tells you, the slopes are actually somewhat different:

enter image description here

Graph code:

tw (lfit lne d if inrange(d,-.2,0)) (lfit lne d if inrange(d,0,.2)), legend(off) ylab(#15, angle(0)) ytitle("lne") xtitle("d")

You may want something like my third specification (though it it not clear what you have in mind with the comparison):

. use votex, clear
(102nd Congress)

. /* RD/local linear regression model */
. rd lne d, mbw(100) bw(0.2) ker(rec)
Two variables specified; treatment is 
assumed to jump from zero to one at Z=0. 

 Assignment variable Z is d
 Treatment variable X_T unspecified
 Outcome variable y is lne

Estimating for bandwidth .2
------------------------------------------------------------------------------
         lne |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       lwald |  -.1046939   .1147029    -0.91   0.361    -.3295075    .1201197
------------------------------------------------------------------------------

. 
. /* OLS Version With Interactions */
. reg lne c.d##i.win if d > -.2 & d < .2 // note that you can specify interaction on the fly

      Source |       SS       df       MS              Number of obs =     267
-------------+------------------------------           F(  3,   263) =    0.43
       Model |  .271662281     3  .090554094           Prob > F      =  0.7339
    Residual |  55.7885045   263  .212123591           R-squared     =  0.0048
-------------+------------------------------           Adj R-squared = -0.0065
       Total |  56.0601668   266  .210752507           Root MSE      =  .46057

------------------------------------------------------------------------------
         lne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           d |     .84506   .7855123     1.08   0.283    -.7016333    2.391753
       1.win |  -.1046939   .1257913    -0.83   0.406    -.3523801    .1429923
             |
     win#c.d |
          1  |  -.8707604   1.048807    -0.83   0.407    -2.935887    1.194366
             |
       _cons |   21.44195   .0925378   231.71   0.000     21.25974    21.62415
------------------------------------------------------------------------------

. 
. /* OLS Model Without Interaction */
. reg lne d if d >= -.2 & d < 0 // fit a line to the left

      Source |       SS       df       MS              Number of obs =     109
-------------+------------------------------           F(  1,   107) =    1.58
       Model |  .245503732     1  .245503732           Prob > F      =  0.2116
    Residual |  16.6357215   107  .155474033           R-squared     =  0.0145
-------------+------------------------------           Adj R-squared =  0.0053
       Total |  16.8812252   108  .156307641           Root MSE      =   .3943

------------------------------------------------------------------------------
         lne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           d |     .84506   .6724925     1.26   0.212    -.4880779    2.178198
       _cons |   21.44195   .0792234   270.65   0.000     21.28489      21.599
------------------------------------------------------------------------------

. reg lne d if d >= 0 & d < .2 // fit a line to the right

      Source |       SS       df       MS              Number of obs =     158
-------------+------------------------------           F(  1,   156) =    0.00
       Model |  .000290102     1  .000290102           Prob > F      =  0.9729
    Residual |   39.152783   156  .250979378           R-squared     =  0.0000
-------------+------------------------------           Adj R-squared = -0.0064
       Total |  39.1530731   157  .249382631           Root MSE      =  .50098

------------------------------------------------------------------------------
         lne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
           d |  -.0257003    .755932    -0.03   0.973    -1.518883    1.467483
       _cons |   21.33725   .0926828   230.22   0.000     21.15418    21.52033
------------------------------------------------------------------------------

. 
. di  "RD is "21.33725 - 21.44195 // TE is the diff in the intercepts 
RD is -.1047
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  • $\begingroup$ Thank you very much for the explanation. In your rd command, I wonder whether degree (1) option is mandatory. I checked with different numbers (2,3,..10), but it give the same answer. $\endgroup$
    – user227710
    Feb 10 '14 at 15:28
  • 2
    $\begingroup$ Indeed. The degree option does not actually do anything. I'll take it out. rd always implements local linear regression. If you want higher order polynomials, try rdrobust lne d, c(0) p(2) kernel(uniform) h(0.2). $\endgroup$
    – dimitriy
    Feb 10 '14 at 18:07

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