joint significance of the dummy variables

[Denis Beuca]

Hi, How do I test for the joint significance of the dummy variables using Stata commands?


Denis

[William Lisowski]

Perhaps with the test command, or in this case with the slightly easier to type testparm command.

. sysuse auto, clear
(1978 automobile data)

. regress price mpg i.rep78

      Source |       SS           df       MS      Number of obs   =        69
-------------+----------------------------------   F(5, 63)        =      4.39
       Model |   149020603         5  29804120.7   Prob > F        =    0.0017
    Residual |   427776355        63  6790100.88   R-squared       =    0.2584
-------------+----------------------------------   Adj R-squared   =    0.1995
       Total |   576796959        68  8482308.22   Root MSE        =    2605.8

------------------------------------------------------------------------------
       price | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         mpg |  -280.2615   61.57666    -4.55   0.000    -403.3126   -157.2103
             |
       rep78 |
          2  |   877.6347   2063.285     0.43   0.672     -3245.51     5000.78
          3  |   1425.657   1905.438     0.75   0.457    -2382.057    5233.371
          4  |   1693.841   1942.669     0.87   0.387    -2188.274    5575.956
          5  |   3131.982   2041.049     1.53   0.130    -946.7282    7210.693
             |
       _cons |   10449.99   2251.041     4.64   0.000     5951.646    14948.34
------------------------------------------------------------------------------

. testparm i.rep78

 ( 1)  2.rep78 = 0
 ( 2)  3.rep78 = 0
 ( 3)  4.rep78 = 0
 ( 4)  5.rep78 = 0

       F(  4,    63) =    1.07
            Prob > F =    0.3780

.

Or if you prefer a likelihood ratio test

. sysuse auto, clear
(1978 automobile data)

. regress price mpg i.rep78

      Source |       SS           df       MS      Number of obs   =        69
-------------+----------------------------------   F(5, 63)        =      4.39
       Model |   149020603         5  29804120.7   Prob > F        =    0.0017
    Residual |   427776355        63  6790100.88   R-squared       =    0.2584
-------------+----------------------------------   Adj R-squared   =    0.1995
       Total |   576796959        68  8482308.22   Root MSE        =    2605.8

------------------------------------------------------------------------------
       price | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         mpg |  -280.2615   61.57666    -4.55   0.000    -403.3126   -157.2103
             |
       rep78 |
          2  |   877.6347   2063.285     0.43   0.672     -3245.51     5000.78
          3  |   1425.657   1905.438     0.75   0.457    -2382.057    5233.371
          4  |   1693.841   1942.669     0.87   0.387    -2188.274    5575.956
          5  |   3131.982   2041.049     1.53   0.130    -946.7282    7210.693
             |
       _cons |   10449.99   2251.041     4.64   0.000     5951.646    14948.34
------------------------------------------------------------------------------

. estimates store full

. regress price mpg if e(sample)

      Source |       SS           df       MS      Number of obs   =        69
-------------+----------------------------------   F(1, 67)        =     17.58
       Model |   119910002         1   119910002   Prob > F        =    0.0001
    Residual |   456886957        67  6819208.31   R-squared       =    0.2079
-------------+----------------------------------   Adj R-squared   =    0.1961
       Total |   576796959        68  8482308.22   Root MSE        =    2611.4

------------------------------------------------------------------------------
       price | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         mpg |  -226.3607   53.98091    -4.19   0.000     -334.107   -118.6143
       _cons |   10965.23   1191.468     9.20   0.000      8587.05    13343.41
------------------------------------------------------------------------------

. estimates store part

. lrtest full part

Likelihood-ratio test
Assumption: part nested within full

 LR chi2(4) =   4.54
Prob > chi2 = 0.3375

.

[Denis Beuca]

Thanks for the reply, just asking if, for example, I would test for the joint significance of the qual1 dummy variables and for the joint significance of the region dummy variables. How would I lay out the commands in Stata?

[Richard Williams]

It might help if you wrote out the estimation command you have in mind. But based on what you say,

reg y i.qual i.region
testparm i.qual
testparm i.region
testparm i.qual i.region