names of interacting variables

[Costas Antoniou]

Hello all,

I am estimating a regression model with interactions as below:

eststo: reghdfe y i.x1##i.x2##c.x3, ab() cl()

This model would include as regressors x1,x2,x3, x1*x2, x1*x3, x2*x3 and x1*x2*x3 (7 variables).

I am trying to export the results into a Latex table, but I don't want all the variables to be included.

How can I see how Stata names all these interaction variables so that I can choose the ones I want?

Thank you

Costas

[Carlo Lazzaro]

Costas:
you can inspect the name of the coefficienst via -mat list e(b)-:

. use "https://www.stata-press.com/data/r18/nlswork.dta"
(National Longitudinal Survey of Young Women, 14-24 years old in 1968)

. reghdfe ln_wage c.age##c.age i.year, abs(idcode) vce(cluster idcode)
(dropped 551 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =     27,959
Absorbing 1 HDFE group                            F(  16,   4158) =      79.11
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6593
                                                  Adj R-squared   =     0.5995
                                                  Within R-sq.    =     0.1162
Number of clusters (idcode)  =      4,159         Root MSE        =     0.3013

                             (Std. err. adjusted for 4,159 clusters in idcode)
------------------------------------------------------------------------------
             |               Robust
     ln_wage | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         age |   .0728746   .0136873     5.32   0.000     .0460402    .0997089
             |
 c.age#c.age |  -.0010113   .0001076    -9.39   0.000    -.0012224   -.0008003
             |
        year |
         69  |   .0647054   .0155252     4.17   0.000     .0342677    .0951432
         70  |   .0284423   .0264645     1.07   0.283    -.0234422    .0803268
         71  |   .0579959   .0384118     1.51   0.131    -.0173118    .1333037
         72  |   .0510671   .0502685     1.02   0.310    -.0474861    .1496203
         73  |   .0424104   .0624936     0.68   0.497    -.0801104    .1649313
         75  |   .0151376   .0862297     0.18   0.861    -.1539187    .1841939
         77  |   .0340933   .1106863     0.31   0.758    -.1829111    .2510976
         78  |   .0537334   .1232256     0.44   0.663    -.1878546    .2953214
         80  |   .0369475   .1473754     0.25   0.802    -.2519871    .3258822
         82  |   .0391687   .1715655     0.23   0.819    -.2971914    .3755288
         83  |    .058766   .1836122     0.32   0.749    -.3012121    .4187442
         85  |   .1042758    .208024     0.50   0.616    -.3035625     .512114
         87  |   .1242272   .2327373     0.53   0.594    -.3320624    .5805167
         88  |   .1904977   .2486132     0.77   0.444    -.2969171    .6779125
             |
       _cons |   .3956251   .2469216     1.60   0.109    -.0884733    .8797234
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      idcode |      4159        4159           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. mat list e(b)

e(b)[1,18]
                     c.age#        68b.         69.         70.         71.         72.         73.         75.         77.         78.         80.         82.         83.
           age       c.age        year        year        year        year        year        year        year        year        year        year        year        year
y1   .07287457  -.00101134           0   .06470541   .02844226   .05799593   .05106711   .04241042    .0151376   .03409325   .05373339   .03694753   .03916873   .05876604

            85.         87.         88.            
          year        year        year       _cons
y1    .1042758   .12422716   .19049771   .39562505

.

[Costas Antoniou]

Thank you for this.
However, it does not seem to work.
For example, say that I want to test post estimation if the coefficient on the interaction c.age#c.age is equal to 1.
If I use test c.age#c.age=1, Stata gives me an error saying that c.age#c.age is an invalid name.
Thanks again.
Costas

[Andrew Musau]

test _b[c.age#c.age]=1

See

help _variables

[Andrew Musau]

Reading #1, Carlo's response was appropriate. You then changed this to a question about the test command. I suggest that you provide a reproducible example where referring to the coefficient on the interaction term failed.

[Rich Goldstein]

I'm rather confused and don't think we are being shown what is actually going on; here is a simple example:

. sysuse auto
(1978 automobile data)
r; t=0.03 10:25:59

. reghdfe price c.weight##c.weight
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =         74
Absorbing 1 HDFE group                            F(   2,     71) =      23.09
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3941
                                                  Adj R-squared   =     0.3770
                                                  Within R-sq.    =     0.3941
                                                  Root MSE        =  2327.9681

------------------------------------------------------------------------------
       price | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      weight |  -7.273097   2.691747    -2.70   0.009    -12.64029   -1.905906
             |
    c.weight#|
    c.weight |   .0015142   .0004337     3.49   0.001     .0006494     .002379
             |
       _cons |    13418.8   3997.822     3.36   0.001     5447.372    21390.23
------------------------------------------------------------------------------
r; t=0.20 10:26:16

. test c.weight#c.weight=1

 ( 1)  c.weight#c.weight = 1

       F(  1,    71) = 5.3e+06
            Prob > F =    0.0000