Hello!

I am doing research on corruption and have a balanced panel data set of 363 observations for 33 countries and 11-year span. I was not sure what model to use but after some tests that rejected OLS (fair enough, it is a panel) and RE I decided to go for fixed effects model. Tests on heteroskedasticity and autocorrelation showed presence on both. I was looking for solutions for that and found a thread on this forum that had an answer from Carlo that said fixed effects with cluster adjusted standard errors adjust for both of those effects so I settled for FE model with clustered standard errors.

Thereafter I ran that model with time dummies (i.year) and the model showed that last 6 years are significant at 0.000 levels, which I guess means that there is severe (?) autocorrelation? Now I am stuck and unsure how to go on from here. Should I forget about time dummies and continue with the FE that adjusts for clusters and report results on that or should I report the results with the time dummies? The difference is that one of my main independent variables that was significant and is such in most previous research on corruption not significant anymore. Should I perhaps go for another model that might fit better or is the model good enough and I should stick to it?

I am very new with stata and fairly new with econometrics so any tips would help and mean a lot!
Thank you heaps in advance!

Code:
. xtreg corr fdipct faidpct loggdppc trade rule natres ethnic i.year, fe vce(cluster c_id)
note: ethnic omitted because of collinearity

Fixed-effects (within) regression               Number of obs     =        363
Group variable: c_id                            Number of groups  =         33

R-sq:                                           Obs per group:
     within  = 0.5078                                         min =         11
     between = 0.6435                                         avg =       11.0
     overall = 0.5920                                         max =         11

                                                F(16,32)          =      18.55
corr(u_i, Xb)  = 0.3889                         Prob > F          =     0.0000

                                  (Std. Err. adjusted for 33 clusters in c_id)
------------------------------------------------------------------------------
             |               Robust
        corr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      fdipct |  -.0361876   .0147794    -2.45   0.020    -.0662922    -.006083
     faidpct |  -.0209003   .0459333    -0.46   0.652    -.1144634    .0726628
    loggdppc |  -2.380897   3.963144    -0.60   0.552    -10.45356    5.691764
       trade |   .0236044   .0208849     1.13   0.267    -.0189367    .0661455
        rule |  -9.406962   2.827707    -3.33   0.002    -15.16681   -3.647111
      natres |  -.1399976   .0717829    -1.95   0.060    -.2862145    .0062194
      ethnic |          0  (omitted)
             |
        year |
       2008  |   .3452493   .5045135     0.68   0.499    -.6824111     1.37291
       2009  |  -.6404473   .5801911    -1.10   0.278    -1.822258    .5413634
       2010  |  -1.131243   .8078917    -1.40   0.171    -2.776865    .5143785
       2011  |  -1.485195   1.013161    -1.47   0.152    -3.548936    .5785452
       2012  |  -5.358887   1.232563    -4.35   0.000    -7.869536   -2.848238
       2013  |  -5.224643   1.229671    -4.25   0.000    -7.729402   -2.719885
       2014  |  -4.625518   1.182611    -3.91   0.000    -7.034417   -2.216619
       2015  |  -4.594838   1.304793    -3.52   0.001    -7.252614   -1.937063
       2016  |  -5.051435   1.520751    -3.32   0.002    -8.149104   -1.953766
       2017  |  -5.483272   1.687407    -3.25   0.003    -8.920407   -2.046136
             |
       _cons |   86.17475   30.16134     2.86   0.007     24.73811    147.6114
-------------+----------------------------------------------------------------
     sigma_u |   5.983148
     sigma_e |  3.0626503
         rho |  .79238014   (fraction of variance due to u_i)
------------------------------------------------------------------------------

. testparm(i.year)

 ( 1)  2008.year = 0
 ( 2)  2009.year = 0
 ( 3)  2010.year = 0
 ( 4)  2011.year = 0
 ( 5)  2012.year = 0
 ( 6)  2013.year = 0
 ( 7)  2014.year = 0
 ( 8)  2015.year = 0
 ( 9)  2016.year = 0
 (10)  2017.year = 0

       F( 10,    32) =    6.21
            Prob > F =    0.0000