I have panel data on women measured across 3 waves. I am unsure as to whether to model them with fixed or random effects models. I cluster my regression at the respondents current county so I cannot use a Hausman test, I attempt to use the Mundlak approach as described on the Stata blog (https://blog.stata.com/2015/10/29/fi...dlak-approach/)

On the blog, computing the test is described as follows:
  1. Compute the panel-level average of your time-varying covariates.
  2. Use a random-effects estimator to regress your covariates and the panel-level means generated in (1) against your outcome.
  3. Test that the panel-level means generated in (1) are jointly zero.
But when I do this, I find the following results:

Code:
capture drop mean_psum_y
bysort id: egen mean_psum_y = mean(psum_unemployed_total_cont_y)
(108 missing values generated)

capture drop mean_age_y
bysort id: egen mean_age_y = mean(age_y)
(183 missing values generated)

capture drop mean_year
bysort id: egen mean_year = mean(year)

capture drop mean_current_county_y1
bysort id: egen mean_current_county_y1 = mean(current_county_y1)

capture drop mean_own_education_y
bysort id: egen mean_own_education_y = mean(own_education_y)
(90 missing values generated)

Code:
. xtreg binary_health_y psum_unemployed_total_cont_y calt3_other_children_y0 i.year i.current_county_y1 i.own_education_y
>  age_y mean_current_county_y1 mean_own_education_y mean_year mean_age_y mean_psum_y if has_y0_questionnaire==1 & has_y5
> _questionnaire==1 | has_y0_questionnaire==1 & has_y10_questionnaire==1 | has_y0_questionnaire==1 & has_y5_questionnaire
> ==1 & has_y10_questionnaire==1 | has_y0_questionnaire==1 & cbmi_y5 !=. & has_y5_questionnaire==0 | has_y0_questionnaire
> ==1 & cbmi_y10 !=. & has_y10_questionnaire==0 | has_y0_questionnaire==1 & cbmi_y5 !=. & has_y5_questionnaire==0 & cbmi_
> y10 !=. & has_y10_questionnaire==0 | has_y0_questionnaire==1 & cbmi_y5 !=. & has_y5_questionnaire==1 | has_y0_questionn
> aire==1 & cbmi_y10 !=. & has_y10_questionnaire==1 | has_y0_questionnaire==1 & cbmi_y5 !=. & has_y5_questionnaire==1 & c
> bmi_y10 !=. & has_y10_questionnaire==1, cluster (current_county_y1) re
note: mean_current_county_y1 omitted because of collinearity
note: mean_own_education_y omitted because of collinearity
note: mean_year omitted because of collinearity
note: mean_age_y omitted because of collinearity
note: mean_psum_y omitted because of collinearity

Random-effects GLS regression                   Number of obs     =      1,578
Group variable: id                              Number of groups  =        635

R-sq:                                           Obs per group:
     within  = 0.0063                                         min =          1
     between = 0.0892                                         avg =        2.5
     overall = 0.0585                                         max =          3

                                                Wald chi2(9)      =          .
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =          .

                                                               (Std. Err. adjusted for 29 clusters in current_county_y1)
------------------------------------------------------------------------------------------------------------------------
                                                       |               Robust
                                       binary_health_y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------------------------------------+----------------------------------------------------------------
                          psum_unemployed_total_cont_y |   -.011246   .0042776    -2.63   0.009      -.01963    -.002862
                               calt3_other_children_y0 |  -.0132467   .0144022    -0.92   0.358    -.0414744     .014981
                                                       |
                                                  year |
                                                    5  |  -.0797771    .026846    -2.97   0.003    -.1323944   -.0271598
                                                   10  |   .0380614   .0343703     1.11   0.268     -.029303    .1054259
                                                       |
                                     current_county_y1 |
                                                Cavan  |   .3497291   .0472267     7.41   0.000     .2571665    .4422917
                                                Clare  |  -.0653339   .0108904    -6.00   0.000    -.0866788   -.0439891
                                                 Cork  |  -.1672661   .0185473    -9.02   0.000    -.2036181   -.1309142
                                              Donegal  |   .2440473   .0409964     5.95   0.000     .1636959    .3243987
                                          Dublin City  |  -.0319963   .0105698    -3.03   0.002    -.0527128   -.0112799
                               Dún Laoghaire-Rathdown  |  -.0261354    .023202    -1.13   0.260    -.0716106    .0193397
                                               Fingal  |   .0898723   .0135182     6.65   0.000     .0633771    .1163674
                                               Galway  |  -.0208434   .0111599    -1.87   0.062    -.0427165    .0010297
                                          Galway City  |   .0171861   .0085169     2.02   0.044     .0004932     .033879
                                                Kerry  |   .1631191   .0171811     9.49   0.000     .1294448    .1967933
                                              Kildare  |   -.052603   .0137643    -3.82   0.000    -.0795805   -.0256256
                                             Kilkenny  |   -.054465   .0247041    -2.20   0.027    -.1028841   -.0060459
                                                Laois  |  -.1723436   .0261493    -6.59   0.000    -.2235953   -.1210919
                                             Limerick  |   .1516061   .0256513     5.91   0.000     .1013305    .2018817
                                             Longford  |   .3243542   .0275731    11.76   0.000      .270312    .3783964
                                                Louth  |   .2935245   .0208773    14.06   0.000     .2526058    .3344432
                                                 Mayo  |  -.0083414   .0164228    -0.51   0.612    -.0405295    .0238468
                                                Meath  |  -.0115489   .0164661    -0.70   0.483    -.0438219    .0207242
                                             Monaghan  |   -.393205   .0254279   -15.46   0.000    -.4430427   -.3433672
                                               Offaly  |   -.117562   .0079983   -14.70   0.000    -.1332383   -.1018857
                                            Roscommon  |   .1387658   .0161138     8.61   0.000     .1071833    .1703483
                                                Sligo  |  -.8495427    .023403   -36.30   0.000    -.8954118   -.8036736
                                         South Dublin  |  -.1471678   .0090874   -16.19   0.000    -.1649788   -.1293568
                                      Tipperary North  |   .1399979   .0234896     5.96   0.000     .0939591    .1860366
                                            Waterford  |  -.0394648   .0214446    -1.84   0.066    -.0814955    .0025659
                                            Westmeath  |  -.0384343   .0119437    -3.22   0.001    -.0618435   -.0150252
                                              Wexford  |   .0530227   .0179453     2.95   0.003     .0178506    .0881948
                                              Wicklow  |  -.0002635   .0148818    -0.02   0.986    -.0294314    .0289043
                                                       |
                                       own_education_y |
                                         No schooling  |          0  (empty)
                             Primary school education  |   .4571419   .2121907     2.15   0.031     .0412558    .8730279
                                Some secondary school  |   .6485139   .0774671     8.37   0.000     .4966812    .8003467
                         Complete secondary education  |   .6711843   .1152782     5.82   0.000     .4452432    .8971255
Some third level education at college, university, ..  |   .7155908    .125676     5.69   0.000     .4692704    .9619112
Complete third level education at college, universi..  |   .8162614   .1183988     6.89   0.000     .5842041    1.048319
                                                       |
                                                 age_y |   .0049239   .0039453     1.25   0.212    -.0028088    .0126565
                                mean_current_county_y1 |          0  (omitted)
                                  mean_own_education_y |          0  (omitted)
                                             mean_year |          0  (omitted)
                                            mean_age_y |          0  (omitted)
                                           mean_psum_y |          0  (omitted)
                                                 _cons |          0  (omitted)
-------------------------------------------------------+----------------------------------------------------------------
                                               sigma_u |  .25259967
                                               sigma_e |  .35438184
                                                   rho |  .33690033   (fraction of variance due to u_i)
------------------------------------------------------------------------------------------------------------------------

. 
. capture drop mundlak

. estimates store mundlak
Below I test the joint significance of the added means as instructed on the Stata blog, and this is where I have my problem

Code:

. test mean_psum_y  mean_age_y mean_year mean_current_county_y1 mean_own_education_y

 ( 1)  o.mean_psum_y = 0
 ( 2)  o.mean_age_y = 0
 ( 3)  o.mean_year = 0
 ( 4)  o.mean_current_county_y1 = 0
 ( 5)  o.mean_own_education_y = 0
       Constraint 1 dropped
       Constraint 2 dropped
       Constraint 3 dropped
       Constraint 4 dropped
       Constraint 5 dropped

           chi2(  0) =       .
         Prob > chi2 =         .
As you can see there is no output at all for this test.

Previously I had included more controls (which I now exclude due to endogeneity fears) and had gotten output from this test. Can anyone advise me why I am getting no results now and how to remedy this?

Best,

John