Dear statalisters,

My questions is about Walds test and how to use it properly?

I have performed some mixed linear regression models like the following.

Code:
webuse auto
Code:
mixed price mpg weight i.rep78 || foreign:

HTML Code:
Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -628.14785  
Iteration 1:   log likelihood = -628.14785  

Computing standard errors:

Mixed-effects ML regression                     Number of obs     =         69
Group variable: foreign                         Number of groups  =          2

                                                Obs per group:
                                                              min =         21
                                                              avg =       34.5
                                                              max =         48

                                                Wald chi2(3)      =      63.90
Log likelihood = -628.14785                     Prob > chi2       =     0.0000

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         mpg |   17.59927   75.57848     0.23   0.816    -130.5318    165.7304
      weight |   3.387514   .6326358     5.35   0.000     2.147571    4.627458
       rep78 |    206.673   321.9493     0.64   0.521    -424.3361    837.6821
       _cons |  -4599.017   3437.865    -1.34   0.181    -11337.11    2139.075
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
foreign: Identity            |
                  var(_cons) |    2526074    2889011      268501.8    2.38e+07
-----------------------------+------------------------------------------------
               var(Residual) |    4338805     751500       3089856     6092590
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 8.00          Prob >= chibar2 = 0.0023

Code:
mixed price mpg weight i.rep78 || foreign: , coeflegend
HTML Code:
Output left out on purpose
Can I use the Wald test (as presented below) to say something about wether rep78 category 2 affect the price more than rep78 category 3 or category 4 and so on? Or can I only use the Wald test to exclude variables from the regression model?

Code:
test _b[price:mpg] = _b[price:mpg]+_b[price:2.rep78] =_b[price:mpg]+_b[price:3.rep78] =_b[price:mpg]+_b[price:4.rep78] , mtest(bon)
HTML Code:
 ( 1)  - [price]2.rep78 = 0
 ( 2)  - [price]3.rep78 = 0
 ( 3)  - [price]4.rep78 = 0

---------------------------------------
       |        chi2     df       p
-------+-------------------------------
  (1)  |        0.12      1     1.0000 #
  (2)  |        0.35      1     1.0000 #
  (3)  |        0.19      1     1.0000 #
-------+-------------------------------
  all  |        0.49      3     0.9222
---------------------------------------
         # Bonferroni-adjusted p-values

I prefer to avoid the likelihood ratio test modellings.



best regards