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  • #1

    tes

    Code:
          Source |       SS       df       MS              Number of obs =    1275
-------------+------------------------------           F( 20,  1254) =   55.70
       Model |  1126.16452    20  56.3082259           Prob > F      =  0.0000
    Residual |  1267.70928  1254  1.01093244           R-squared     =  0.4704
-------------+------------------------------           Adj R-squared =  0.4620
       Total |   2393.8738  1274  1.87902182           Root MSE      =  1.0055

---------------------------------------------------------------------------------
lntransferamo~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
            age |   .6950561   .0925588     7.51   0.000      .513469    .8766433
          agesq |  -.0164139   .0018991    -8.64   0.000    -.0201397   -.0126881
     careerapps |   .0043794   .0006616     6.62   0.000     .0030815    .0056774
         psapps |   .0064421   .0035433     1.82   0.069    -.0005093    .0133936
   goalsperapps |     .83733   .3631742     2.31   0.021      .124834    1.549826
 psgoalsperapps |   .7985406   .2964837     2.69   0.007     .2168818    1.380199
   cardsperapps |   .5686357   .2578686     2.21   0.028     .0627342    1.074537
       previous |  -.0329326   .0272717    -1.21   0.227    -.0864357    .0205705
  international |   .7820627   .0637527    12.27   0.000      .656989    .9071363
    stadiumsize |   7.84e-06   1.92e-06     4.09   0.000     4.07e-06    .0000116
    samecountry |  -.0356082   .0607103    -0.59   0.558    -.1547131    .0834967
championsleague |   .7544761   .0800538     9.42   0.000      .597422    .9115302
                |
        country |
        France  |  -.9265981   .0967576    -9.58   0.000    -1.116423   -.7367734
       Germany  |  -.8793806   .0882539    -9.96   0.000    -1.052522    -.706239
         Italy  |  -.6791012   .0871997    -7.79   0.000    -.8501745   -.5080279
         Spain  |  -.5508845   .0980953    -5.62   0.000    -.7433336   -.3584355
                |
 transferwindow |
        2014m1  |  -.2159898   .1385922    -1.56   0.119    -.4878878    .0559083
        2014m8  |   .0572702   .0743902     0.77   0.442    -.0886727    .2032131
        2015m1  |     .29499   .1415783     2.08   0.037     .0172336    .5727464
        2015m8  |   .3223026    .072375     4.45   0.000     .1803132    .4642921
                |
          _cons |   5.860026   1.117088     5.25   0.000     3.668458    8.051594
    Tags: None
  • #2
    Carlo

    For my Pooled OLS regression I got the following from Stata:

    Code:
          Source |       SS       df       MS              Number of obs =    1275
-------------+------------------------------           F( 20,  1254) =   55.70
       Model |  1126.16452    20  56.3082259           Prob > F      =  0.0000
    Residual |  1267.70928  1254  1.01093244           R-squared     =  0.4704
-------------+------------------------------           Adj R-squared =  0.4620
       Total |   2393.8738  1274  1.87902182           Root MSE      =  1.0055

---------------------------------------------------------------------------------
lntransferamo~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
            age |   .6950561   .0925588     7.51   0.000      .513469    .8766433
          agesq |  -.0164139   .0018991    -8.64   0.000    -.0201397   -.0126881
     careerapps |   .0043794   .0006616     6.62   0.000     .0030815    .0056774
         psapps |   .0064421   .0035433     1.82   0.069    -.0005093    .0133936
   goalsperapps |     .83733   .3631742     2.31   0.021      .124834    1.549826
 psgoalsperapps |   .7985406   .2964837     2.69   0.007     .2168818    1.380199
   cardsperapps |   .5686357   .2578686     2.21   0.028     .0627342    1.074537
       previous |  -.0329326   .0272717    -1.21   0.227    -.0864357    .0205705
  international |   .7820627   .0637527    12.27   0.000      .656989    .9071363
    stadiumsize |   7.84e-06   1.92e-06     4.09   0.000     4.07e-06    .0000116
    samecountry |  -.0356082   .0607103    -0.59   0.558    -.1547131    .0834967
championsleague |   .7544761   .0800538     9.42   0.000      .597422    .9115302
                |
        country |
        France  |  -.9265981   .0967576    -9.58   0.000    -1.116423   -.7367734
       Germany  |  -.8793806   .0882539    -9.96   0.000    -1.052522    -.706239
         Italy  |  -.6791012   .0871997    -7.79   0.000    -.8501745   -.5080279
         Spain  |  -.5508845   .0980953    -5.62   0.000    -.7433336   -.3584355
                |
 transferwindow |
        2014m1  |  -.2159898   .1385922    -1.56   0.119    -.4878878    .0559083
        2014m8  |   .0572702   .0743902     0.77   0.442    -.0886727    .2032131
        2015m1  |     .29499   .1415783     2.08   0.037     .0172336    .5727464
        2015m8  |   .3223026    .072375     4.45   0.000     .1803132    .4642921
                |
          _cons |   5.860026   1.117088     5.25   0.000     3.668458    8.051594
    For my Fixed Effects regression I got the following:

    Code:
    Fixed-effects (within) regression               Number of obs      =      5810
Group variable: player                          Number of groups   =      1162

R-sq:  within  = 0.0497                         Obs per group: min =         5
       between = 0.1612                                        avg =       5.0
       overall = 0.1492                                        max =         5

                                                F(20,4628)         =     12.11
corr(u_i, Xb)  = -0.3042                        Prob > F           =    0.0000

---------------------------------------------------------------------------------
lntransferamo~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
            age |   1.460599    .257391     5.67   0.000     .9559897    1.965208
          agesq |  -.0265802   .0052086    -5.10   0.000    -.0367915   -.0163688
     careerapps |  -.0005802   .0013987    -0.41   0.678    -.0033223    .0021618
         psapps |   .0108882   .0055404     1.97   0.049     .0000264      .02175
   goalsperapps |  -1.449421   1.132375    -1.28   0.201    -3.669415    .7705734
 psgoalsperapps |    .501842   .3727046     1.35   0.178    -.2288368    1.232521
       previous |   .1298468    .078863     1.65   0.100    -.0247623     .284456
  international |   .1455624   .3763043     0.39   0.699    -.5921734    .8832982
   cardsperapps |   .6642187   1.473011     0.45   0.652    -2.223586    3.552023
    stadiumsize |   8.83e-06   2.34e-06     3.77   0.000     4.24e-06    .0000134
    samecountry |   -.191992   .0864762    -2.22   0.026    -.3615266   -.0224574
championsleague |   .1056238   .1054431     1.00   0.317     -.101095    .3123426
                |
        country |
        France  |  -.0701702   .1626089    -0.43   0.666    -.3889611    .2486207
       Germany  |  -.3350231   .1407158    -2.38   0.017    -.6108932    -.059153
         Italy  |  -.5809121   .1491099    -3.90   0.000    -.8732385   -.2885857
         Spain  |  -.3549929   .1437163    -2.47   0.014    -.6367454   -.0732405
                |
 transferwindow |
        2014m1  |   .0423227    .018704     2.26   0.024      .005654    .0789913
        2014m8  |   .0355479   .0187521     1.90   0.058    -.0012151    .0723108
        2015m1  |   .0587483   .0187151     3.14   0.002     .0220577    .0954389
        2015m8  |   .0718503   .0189408     3.79   0.000     .0347174    .1089833
                |
          _cons |  -5.559949   3.182268    -1.75   0.081    -11.79871     .678814
----------------+----------------------------------------------------------------
        sigma_u |  1.1924223
        sigma_e |  .45002305
            rho |  .87532519   (fraction of variance due to u_i)
---------------------------------------------------------------------------------
F test that all u_i=0:     F(1161, 4628) =    21.06          Prob > F = 0.0000

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