[Carlo Lazzaro]

Niels:
before switching to -xtgls- despite havibng a large N, small T dataset,, please note the dramatically different times (in seconds) taken by -xtreg- and -xtgls- to perform the same simple panel data regression:

. set rmsg on
r; t=0.00 15:48:21

. xtreg ln_wage i.race, re

Random-effects GLS regression                   Number of obs     =     28,534
Group variable: idcode                          Number of groups  =      4,711

R-sq:                                           Obs per group:
     within  = 0.0000                                         min =          1
     between = 0.0198                                         avg =        6.1
     overall = 0.0186                                         max =         15

                                                Wald chi2(2)      =      99.02
corr(u_i, X)   = 0 (assumed)                    Prob > chi2       =     0.0000

------------------------------------------------------------------------------
     ln_wage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        race |
      black  |  -.1300382    .013486    -9.64   0.000    -.1564702   -.1036062
      other  |   .1011474   .0562889     1.80   0.072    -.0091768    .2114716
             |
       _cons |   1.691756   .0071865   235.41   0.000     1.677671    1.705841
-------------+----------------------------------------------------------------
     sigma_u |  .38195681
     sigma_e |  .32028665
         rho |  .58714668   (fraction of variance due to u_i)
------------------------------------------------------------------------------
r; t=0.61 15:48:28

. xtgls ln_wage i.race

Cross-sectional time-series FGLS regression

Coefficients:  generalized least squares
Panels:        homoskedastic
Correlation:   no autocorrelation

Estimated covariances      =         1          Number of obs     =     28,534
Estimated autocorrelations =         0          Number of groups  =      4,711
Estimated coefficients     =         3          Obs per group:
                                                              min =          1
                                                              avg =   6.056888
                                                              max =         15
                                                Wald chi2(2)      =     542.80
Log likelihood             =    -19162          Prob > chi2       =     0.0000

------------------------------------------------------------------------------
     ln_wage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        race |
      black  |  -.1427862    .006243   -22.87   0.000    -.1550222   -.1305502
      other  |    .080671   .0274112     2.94   0.003      .026946     .134396
             |
       _cons |   1.714338   .0033339   514.21   0.000     1.707804    1.720873
------------------------------------------------------------------------------
r; t=692.49 16:00:07
.

A possible work-around could be:
-skipping -xttest2- and -xttest3-;
- graphically inspect your residual distribution;
-robustify/cluster your standard errors if you suspect that (especially) heteroskedasticity can bite your results (as said, serial correlation is expected to be a minor nuisance with a short T dimension).

Otherwise, as many econometricians usually do, go -cluster-/-robust- from scratch; with 200 -panelid- you have enough clusters to survive.

[Carlo Lazzaro]

Niels:
you have to replace -xtreg- with -xtgls-, then.

[Niels Meijer]

Carlo, once again thank you for the quick response.

xttest2: 'XTTEST2': module to perform Breusch-Pagan LM test for cross-sectional correlation in fixed effects model

I forgot to mention that I am planning on using a random effects estimator, so it appears that this method is not suitable for me I suppose? Same goes for xttest3.

[Carlo Lazzaro]

Niels:
see the user-written programmes -xttest2- and -xttest3-.

[Niels Meijer]

Carlo, thank you for taking the time to reply I appreciate it.

Your assumptions are indeed correct, default risk is a score. I was wondering if a Breusch-Godfrey and Breusch-Pagan test would be suitable? Or alternatives? As this is in regards to my thesis, I prefer using objective tests as opposed to analyzing patterns. This would also allow me to incorporate the results of these tests into my thesis.

[Carlo Lazzaro]

Niels:
you seem to have a large N, small T panel dataset: hence, assuming a continuous dependent variable (that is, a score for default risk), I would go -xtreg-.
You can graphically inspect your residual distribution and see whether a heteroskedasticity-suggestive pattern comes alive.
If that were the case, you can robustifying/clustering your standard errors (these options do the same job under -xtreg-) and account for heteroskedasticity and/or autocorrelation (the latter is usually a minor nuisance in panels like the the you're supposed to deal with).
In my assumptions about yor panel were incorrect (say, your dependent variable is categorical: default risk yes/no), -please provide the list ith furthere details.