Hi all,

I have a Fractional Response Model where my dependent variable is bounded between 0 and 1 - with a lot of zeros.

Some of my independent variables lie between 0 and 1 as well, but not all of them.

Should I go for
Code:
margins, dydx(*)
or
Code:
margins, dyex(*)
?

Here is the output as well:

Code:
 fracreg logit y x1 x2 x3 x4 if datayearfiscal==2008

Iteration 0:   log pseudolikelihood = -197.08665  
Iteration 1:   log pseudolikelihood = -182.02928  
Iteration 2:   log pseudolikelihood = -181.99607  
Iteration 3:   log pseudolikelihood = -181.99607  

Fractional logistic regression                  Number of obs     =        279
                                                Wald chi2(4)      =      87.36
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -181.99607               Pseudo R2         =     0.0589

------------------------------------------------------------------------------
             |               Robust
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          x1 |   1.254019   .3392668     3.70   0.000     .5890682     1.91897
          x2 |  -2.369368   .9091443    -2.61   0.009    -4.151258   -.5874784
          x3 |   .1100148   .1341461     0.82   0.412    -.1529068    .3729364
          x4 |  -2.741405   .4381923    -6.26   0.000    -3.600246   -1.882564
       _cons |   .2069838   .2960355     0.70   0.484    -.3732351    .7872028
------------------------------------------------------------------------------
x1, x2, and x3 lie between 0 and 1 while x4 does not have bounds.


Code:
 margins, dydx(*)

Average marginal effects                        Number of obs     =        279
Model VCE    : Robust

Expression   : Conditional mean of y, predict()
dy/dx w.r.t. : x1 x2 x3 x4

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          x1 |   .2888261   .0765901     3.77   0.000     .1387123    .4389399
          x2 |  -.5457139      .2072    -2.63   0.008    -.9518185   -.1396092
          x3 |   .0253387   .0308573     0.82   0.412    -.0351405    .0858178
          x4 |  -.6314015   .0943868    -6.69   0.000    -.8163962   -.4464069
------------------------------------------------------------------------------
Code:
 margins, dyex(*)

Average marginal effects                        Number of obs     =        279
Model VCE    : Robust

Expression   : Conditional mean of y, predict()
dy/ex w.r.t. : x1 x2 x3 x4

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/ex   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          x1 |   .1726102   .0459446     3.76   0.000     .0825605    .2626599
          x2 |   -.061035   .0229026    -2.66   0.008    -.1059233   -.0161466
          x3 |   .0125208   .0152707     0.82   0.412    -.0174091    .0424508
          x4 |  -.1666824   .0238142    -7.00   0.000    -.2133574   -.1200074
------------------------------------------------------------------------------
Is the following correct for the dyex?
1% increase in x1 increases the value of y by 0.173

Does that make sense to talk about 1% change in x1 while it is already in percentage as well. Would it be better to go for dydx, instead?

Thanks a lot.