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    Hello! I apologize if some of the questions in this post seem simple, but this has to do with my thesis and I appreciate any assistance!

    I am dealing with panel data involving some banks over 63 time periods(N=4736 T=63). I used the Hausman test to determine I needed to use a FE model. But, this is where the trouble begins and my questions begin! So I run a basic xtreg fe model and get this result
    Code:
    xtreg zscore lnasset lnassetsq diverse leverage eeffqr DGS10 CPIAUCSL_PCH GDPC1_PC1, fe
    
    Fixed-effects (within) regression               Number of obs     =    298,355
    Group variable: cert                            Number of groups  =      4,736
    
    R-sq:                                           Obs per group:
         within  = 0.0179                                         min =         62
         between = 0.0242                                         avg =       63.0
         overall = 0.0227                                         max =         63
    
                                                    F(8,293611)       =     670.17
    corr(u_i, Xb)  = -0.0196                        Prob > F          =     0.0000
    
    ------------------------------------------------------------------------------
          zscore |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
         lnasset |   .4193168   .0420687     9.97   0.000     .3368634    .5017702
       lnassetsq |  -.0071421   .0016294    -4.38   0.000    -.0103357   -.0039485
         diverse |   .0000122    .000058     0.21   0.834    -.0001015    .0001259
        leverage |   -.013206   .0004068   -32.46   0.000    -.0140033   -.0124087
          eeffqr |  -.0001518   8.33e-06   -18.23   0.000    -.0001681   -.0001355
           DGS10 |   .0478444   .0021882    21.86   0.000     .0435557    .0521332
    CPIAUCSL_PCH |   .0676272   .0032808    20.61   0.000     .0611969    .0740575
       GDPC1_PC1 |   .0310941   .0008967    34.68   0.000     .0293366    .0328516
           _cons |  -1.795671   .2740217    -6.55   0.000    -2.332746   -1.258596
    -------------+----------------------------------------------------------------
         sigma_u |  1.7745564
         sigma_e |  .99459682
             rho |  .76095759   (fraction of variance due to u_i)
    ------------------------------------------------------------------------------
    F test that all u_i=0: F(4735, 293611) = 196.07              Prob > F = 0.0000
    so now I go about running some test to check for serial correlation and heteroscedasticity.
    Code:
    xttest3
    
    Modified Wald test for groupwise heteroskedasticity
    in fixed effect regression model
    
    H0: sigma(i)^2 = sigma^2 for all i
    
    chi2 (4736)  =   379.43
    Prob>chi2 =      1.0000
    Which if I interpret this correctly means my model doesn't suffer from hetero.
    Now running xtserial I get
    Code:
    xtserial zscore lnasset lnassetsq diverse leverage eeffqr DGS10 CPIAUCSL_PCH GDPC1_PC1
    
    Wooldridge test for autocorrelation in panel data
    H0: no first-order autocorrelation
        F(  1,    4735) =    226.237
               Prob > F =      0.0000
    Meaning my model does suffer from autocorrelation. So what now with this information do I run a vce(robust) fe or is there some other tests I should run or other model or option I should use. I am outside my statistical chops currently but I am trying to learn.

    Secondly if I were to use a VCE(Robust) model why when I run it using areg as such do I get such a different significance on some of my variables than using xtreg. It was my impression they were so similar that they should not differ by much?
    Results from areg note that cert is just a unique identifier for each individual bank:
    Code:
    areg zscore lnasset lnassetsq diverse leverage eeffqr DGS10 CPIAUCSL_PCH GDPC1_PC1, a(cert) vce(robust)
    
    Linear regression, absorbing indicators         Number of obs     =    298,355
    Absorbed variable: cert                         No. of categories =      4,736
                                                    F(   8, 293611)   =     343.16
                                                    Prob > F          =     0.0000
                                                    R-squared         =     0.7691
                                                    Adj R-squared     =     0.7654
                                                    Root MSE          =     0.9946
    
    ------------------------------------------------------------------------------
                 |               Robust
          zscore |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
         lnasset |   .4193168   .0686047     6.11   0.000     .2848535    .5537801
       lnassetsq |  -.0071421   .0025061    -2.85   0.004    -.0120541   -.0022301
         diverse |   .0000122   .0000432     0.28   0.778    -.0000725    .0000969
        leverage |   -.013206   .0102436    -1.29   0.197    -.0332831    .0068711
          eeffqr |  -.0001518   .0001045    -1.45   0.146    -.0003566     .000053
           DGS10 |   .0478444   .0046113    10.38   0.000     .0388064    .0568824
    CPIAUCSL_PCH |   .0676272   .0041492    16.30   0.000     .0594948    .0757596
       GDPC1_PC1 |   .0310941   .0011823    26.30   0.000     .0287769    .0334113
           _cons |  -1.795671   .4056216    -4.43   0.000    -2.590678   -1.000664
    ------------------------------------------------------------------------------
    Now using a fe model:
    Code:
    xtreg zscore lnasset lnassetsq diverse leverage eeffqr DGS10 CPIAUCSL_PCH GDPC1_PC1, fe vce(robust)
    
    Fixed-effects (within) regression               Number of obs     =    298,355
    Group variable: cert                            Number of groups  =      4,736
    
    R-sq:                                           Obs per group:
         within  = 0.0179                                         min =         62
         between = 0.0242                                         avg =       63.0
         overall = 0.0227                                         max =         63
    
                                                    F(8,4735)         =     184.31
    corr(u_i, Xb)  = -0.0196                        Prob > F          =     0.0000
    
                                   (Std. Err. adjusted for 4,736 clusters in cert)
    ------------------------------------------------------------------------------
                 |               Robust
          zscore |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
         lnasset |   .4193168    .134761     3.11   0.002     .1551224    .6835111
       lnassetsq |  -.0071421   .0051472    -1.39   0.165    -.0172331    .0029488
         diverse |   .0000122   .0000445     0.27   0.784     -.000075    .0000994
        leverage |   -.013206   .0102906    -1.28   0.199    -.0333804    .0069684
          eeffqr |  -.0001518   .0001059    -1.43   0.152    -.0003594    .0000558
           DGS10 |   .0478444   .0067429     7.10   0.000     .0346253    .0610636
    CPIAUCSL_PCH |   .0676272   .0037723    17.93   0.000     .0602317    .0750227
       GDPC1_PC1 |   .0310941   .0014933    20.82   0.000     .0281667    .0340216
           _cons |  -1.795671   .8540467    -2.10   0.036        -3.47   -.1213422
    -------------+----------------------------------------------------------------
         sigma_u |  1.7745564
         sigma_e |  .99459682
             rho |  .76095759   (fraction of variance due to u_i)
    ------------------------------------------------------------------------------
    I do not understand exactly why in particular lnassetsq became so insignificant in the FE model with robust errors but not in the areg? I am sorry if I am missing something elementary here.
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