hettest and xttest3 not valid

[Matthew Mcilwham]

Hi all,

I am looking to carry out some residual diagnostics on my model

xtreg LGDP l.LGDP K H, fe


I want to carry out some tests for autocorrelation and heteroscedasticity. I am using Stata 15.0 but i cannot seem to utilise any of the following tests with code i find on other forums such as:

ssc install xtserial - cannot find the serial autocorrelation test

ssc install xtest3 - cannot find a test for heteroscedasticity

hettest - invalid

Is there a new way to download the user-written programmes for these tests or are there in-built functions now?


Thanks in advance,
Matt

[Rich Goldstein]

as far as I can see -xtserial- and -xttest3- are from the Stata Journal, not SSC; use -findit- or -search- for locating and downloading; -hettest- (now -estat hettest-) is for use after -regress- and, as far as I know, cannot be used after -xtreg-

[Matthew Mcilwham]

lovely, thank you

[raditya sukmana]

Dear All... i want to run heteroscedasticity (xttest3), i have installed via ssc install and it says "installation complete". however later, when i type "xttest3" it says "last estimates not found" (red color).. can anyone help me..? i have 6 N and 19 Years. i am using stata 14.2

[Carlo Lazzaro]

Raditya:
welcome to this forum.
Please refer to -xttest3- command as a community-contributed module, for reasons that are well expleined in the FAQ. Thanks.
That said, please note that -xttest3- was developed to work after -xtreg, fe- or -xtgls- (quoting from its help file):

. use "https://www.stata-press.com/data/r17/nlswork.dta"
(National Longitudinal Survey of Young Women, 14-24 years old in 1968)

. xtreg ln_wage c.age##c.age, re vce(cluster idcode)

Random-effects GLS regression                   Number of obs     =     28,510
Group variable: idcode                          Number of groups  =      4,710

R-squared:                                      Obs per group:
     Within  = 0.1087                                         min =          1
     Between = 0.1015                                         avg =        6.1
     Overall = 0.0870                                         max =         15

                                                Wald chi2(2)      =    1258.33
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                             (Std. err. adjusted for 4,710 clusters in idcode)
------------------------------------------------------------------------------
             |               Robust
     ln_wage | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         age |   .0590339   .0041049    14.38   0.000     .0509884    .0670795
             |
 c.age#c.age |  -.0006758   .0000688    -9.83   0.000    -.0008107    -.000541
             |
       _cons |   .5479714   .0587198     9.33   0.000     .4328826    .6630601
-------------+----------------------------------------------------------------
     sigma_u |   .3654049
     sigma_e |  .30245467
         rho |  .59342665   (fraction of variance due to u_i)
------------------------------------------------------------------------------

. xttest3
last estimates not xtreg, fe
r(301);

. reg ln_wage c.age##c.age, vce(cluster idcode)

Linear regression                               Number of obs     =     28,510
                                                F(2, 4709)        =     701.39
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0882
                                                Root MSE          =     .45654

                             (Std. err. adjusted for 4,710 clusters in idcode)
------------------------------------------------------------------------------
             |               Robust
     ln_wage | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         age |   .0855891   .0046943    18.23   0.000     .0763861    .0947922
             |
 c.age#c.age |  -.0010982   .0000798   -13.76   0.000    -.0012547   -.0009417
             |
       _cons |   .1647917   .0655679     2.51   0.012      .036248    .2933354
------------------------------------------------------------------------------

. xttest3
last estimates not found
r(301);

. xtreg ln_wage c.age##c.age, fe vce(cluster idcode)

Fixed-effects (within) regression               Number of obs     =     28,510
Group variable: idcode                          Number of groups  =      4,710

R-squared:                                      Obs per group:
     Within  = 0.1087                                         min =          1
     Between = 0.1006                                         avg =        6.1
     Overall = 0.0865                                         max =         15

                                                F(2,4709)         =     507.42
corr(u_i, Xb) = 0.0440                          Prob > F          =     0.0000

                             (Std. err. adjusted for 4,710 clusters in idcode)
------------------------------------------------------------------------------
             |               Robust
     ln_wage | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         age |   .0539076    .004307    12.52   0.000     .0454638    .0623515
             |
 c.age#c.age |  -.0005973    .000072    -8.30   0.000    -.0007384   -.0004562
             |
       _cons |    .639913   .0624195    10.25   0.000     .5175415    .7622845
-------------+----------------------------------------------------------------
     sigma_u |   .4039153
     sigma_e |  .30245467
         rho |  .64073314   (fraction of variance due to u_i)
------------------------------------------------------------------------------

. xttest3

Modified Wald test for groupwise heteroskedasticity
in fixed effect regression model

H0: sigma(i)^2 = sigma^2 for all i

chi2 (4710)  =  4.4e+35
Prob>chi2 =      0.0000


..

[raditya sukmana]

Thanks DR Carlo,,it works,,,many many thanks,,,it really helpful,,,