Hello,

I am using Stata 16.1. When I estimate spatial fixed-effects models or spatial random-effects models using spxtregress, 'z' and 'P>|z|' are not reported for sigma_e for the fixed effects and for sigma_u and sigma_e for random effects, even though '[95% Conf. Interval]' is reported. Could we consider z as coef. / std. err.?

For example, please see the results below.

Code:
. spxtregress lchika Frnk i.year, fe dvarlag(Wid500m) errorlag(Wid500m) ivarlag(Wid500m: Frnk)
  (3796 observations)
  (3796 observations used)
  (data contain 949 panels (places) )
  (weighting matrix defines 949 places)

Performing grid search ... finished

Optimizing concentrated log likelihood:

Iteration 0:   log likelihood =  3018.3537  
Iteration 1:   log likelihood =  3019.1078  
Iteration 2:   log likelihood =  3019.1092  
Iteration 3:   log likelihood =  3019.1092  

Optimizing unconcentrated log likelihood:

Iteration 0:   log likelihood =  3019.1092  
Iteration 1:   log likelihood =  3019.1092  (backed up)

Fixed-effects spatial regression                Number of obs     =      3,796
Group variable: _ID                             Number of groups  =        949
                                                Obs per group     =          4

                                                Wald chi2(6)      =    1567.25
                                                Prob > chi2       =     0.0000
Log likelihood =  3019.1092                     Pseudo R2         =     0.0000

------------------------------------------------------------------------------
      lchika |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lchika       |
        Frnk |   -.008818   .0037451    -2.35   0.019    -.0161583   -.0014776
             |
        year |
       2008  |   .0621063   .0041852    14.84   0.000     .0539035    .0703091
       2013  |  -.0907423   .0041995   -21.61   0.000    -.0989733   -.0825114
       2018  |  -.0150216   .0041734    -3.60   0.000    -.0232014   -.0068419
-------------+----------------------------------------------------------------
Wid500m      |
        Frnk |  -.0143443   .0070367    -2.04   0.041    -.0281358   -.0005527
      lchika |    .238697   .0311096     7.67   0.000     .1777233    .2996708
    e.lchika |   .3770086   .0288928    13.05   0.000     .3203798    .4336374
-------------+----------------------------------------------------------------
    /sigma_e |   .0814047   .0011026                       .079272    .0835947
------------------------------------------------------------------------------
Wald test of spatial terms:          chi2(3) = 544.92     Prob > chi2 = 0.0000

. spxtregress lchika Frnk i.year, re dvarlag(Wid500m) errorlag(Wid500m) ivarlag(Wid500m: Frnk)
  (3796 observations)
  (3796 observations used)
  (data contain 949 panels (places) )
  (weighting matrix defines 949 places)

Fitting starting values:

Iteration 0:   log likelihood =  3018.3537  
Iteration 1:   log likelihood =  3019.1078  
Iteration 2:   log likelihood =  3019.1092  
Iteration 3:   log likelihood =  3019.1092  

Optimizing concentrated log likelihood:

initial:       log likelihood =  624.27272
improve:       log likelihood =  624.27272
rescale:       log likelihood =  624.27272
rescale eq:    log likelihood =  1370.7432
Iteration 0:   log likelihood =  1370.7432  
Iteration 1:   log likelihood =  1434.0376  
Iteration 2:   log likelihood =  1437.9348  
Iteration 3:   log likelihood =  1437.9941  
Iteration 4:   log likelihood =  1437.9942  

Optimizing unconcentrated log likelihood:

Iteration 0:   log likelihood =  1437.9942  
Iteration 1:   log likelihood =  1437.9942  (backed up)

Random-effects spatial regression               Number of obs     =      3,796
Group variable: _ID                             Number of groups  =        949
                                                Obs per group     =          4

                                                Wald chi2(6)      =    1399.21
                                                Prob > chi2       =     0.0000
Log likelihood =  1437.9942                     Pseudo R2         =     0.0052

------------------------------------------------------------------------------
      lchika |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lchika       |
        Frnk |   .0006745   .0038342     0.18   0.860    -.0068404    .0081894
             |
        year |
       2008  |   .0637408   .0042745    14.91   0.000     .0553629    .0721188
       2013  |  -.0943856   .0042683   -22.11   0.000    -.1027513   -.0860199
       2018  |  -.0167213   .0042881    -3.90   0.000    -.0251258   -.0083168
             |
       _cons |    12.5322   .0252436   496.45   0.000     12.48272    12.58167
-------------+----------------------------------------------------------------
Wid500m      |
        Frnk |  -.0039038    .007192    -0.54   0.587    -.0179999    .0101922
      lchika |    .005392   .0036076     1.49   0.135    -.0016787    .0124627
    e.lchika |   .4980093   .0195698    25.45   0.000     .4596532    .5363653
-------------+----------------------------------------------------------------
    /sigma_u |   .6189578   .0144549                      .5912652    .6479474
    /sigma_e |   .0814292   .0011169                      .0792693    .0836481
------------------------------------------------------------------------------
Wald test of spatial terms:          chi2(3) = 674.49     Prob > chi2 = 0.0000

Thank you.