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  • Missing F-statistic when using xtreg with fe, vce(cluster) and adding time-fixed effects

    Dear Statalist user,

    I'm having an unbalanced panel-dataset and want to do a fixed-effects-estimation. I cluster SE at the country-level and also include quarterly time-fixed effects.
    However, when adding time fixed effects, my F-statistic is not reported anymore. I checked the help-file, but do not really see why the F-statistic cannot be calculated.
    My regression looks the following way:
    Code:
    xtreg ln_fdi_l elect_d L1.elect_d  F1.elect_d L2.elect_d  F2.elect_d ///
    data497 data498 data658 data1120 i.q_date , fe vce(cluster  ccode)
    The resulting output is as follows:

    Code:
    Fixed-effects (within) regression               Number of obs     =      9,646
    Group variable: ccode                           Number of groups  =        119
    
    R-sq:                                           Obs per group:
         within  = 0.2433                                         min =          9
         between = 0.4924                                         avg =       81.1
         overall = 0.2709                                         max =        156
    
                                                    F(118,118)        =          .
    corr(u_i, Xb)  = -0.7879                        Prob > F          =          .
    
                                    (Std. Err. adjusted for 119 clusters in ccode)
    ------------------------------------------------------------------------------
                 |               Robust
        ln_fdi_l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
         elect_d |
             --. |   .0114366   .0241773     0.47   0.637     -.036441    .0593142
             L1. |  -.0165092   .0208586    -0.79   0.430    -.0578149    .0247964
             F1. |   .0205511    .026126     0.79   0.433    -.0311854    .0722877
             L2. |  -.0352879   .0272838    -1.29   0.198    -.0893172    .0187415
             F2. |   .0347876   .0277045     1.26   0.212    -.0200748      .08965
                 |
         data497 |   .0073437   .0022485     3.27   0.001     .0028912    .0117963
         data498 |   .0000386   .0000151     2.56   0.012     8.75e-06    .0000684
         data658 |  -.0001534   .0001051    -1.46   0.147    -.0003614    .0000547
        data1120 |   7.05e-09   1.94e-09     3.64   0.000     3.21e-09    1.09e-08
                 |
          q_date |
             63  |  -.0242295   .0206485    -1.17   0.243    -.0651191    .0166601
             64  |  -.0786142   .0343174    -2.29   0.024     -.146572   -.0106565
             65  |  -.1319244   .0485814    -2.72   0.008    -.2281288     -.03572
             66  |   .0243135   .0730829     0.33   0.740    -.1204107    .1690376
             67  |   .0385069   .0716588     0.54   0.592     -.103397    .1804108
             68  |   .0375118   .0609079     0.62   0.539    -.0831024     .158126
             69  |   .0467123   .0629826     0.74   0.460    -.0780103     .171435
             70  |   .0898097   .0652482     1.38   0.171    -.0393994    .2190189
             71  |     .09821   .0706432     1.39   0.167    -.0416828    .2381027
             72  |    .069811   .0652464     1.07   0.287    -.0593946    .1990167
             73  |   .0741895   .0775977     0.96   0.341    -.0794751    .2278541
             74  |   .0921752   .0701882     1.31   0.192    -.0468166    .2311669
             75  |    .084116   .0775115     1.09   0.280    -.0693778    .2376098
             76  |   .0334981    .079253     0.42   0.673    -.1234445    .1904406
             77  |   .0818282   .0794639     1.03   0.305     -.075532    .2391885
             78  |   .0838553    .080813     1.04   0.302    -.0761764     .243887
             79  |   .0870894   .0789471     1.10   0.272    -.0692474    .2434261
             80  |   .0879042    .082177     1.07   0.287    -.0748287    .2506371
             81  |   .0897231   .0916741     0.98   0.330    -.0918165    .2712627
             82  |   .0826496   .1000497     0.83   0.410     -.115476    .2807751
             83  |   .0820208   .0789809     1.04   0.301    -.0743828    .2384244
             84  |   .0936449   .0913912     1.02   0.308    -.0873346    .2746245
             85  |   .0679835   .1159625     0.59   0.559    -.1616538    .2976208
             86  |   .0978981     .10179     0.96   0.338    -.1036739    .2994701
             87  |   .1420955   .1039156     1.37   0.174    -.0636858    .3478768
             88  |   .0114938   .1067359     0.11   0.914    -.1998723    .2228599
             89  |    .097311   .1029148     0.95   0.346    -.1064883    .3011103
             90  |   .0990987   .0932644     1.06   0.290    -.0855903    .2837876
             91  |   .0638884   .1024925     0.62   0.534    -.1390746    .2668514
             92  |   .0400605    .084723     0.47   0.637    -.1277141    .2078351
             93  |   .1125806   .0918243     1.23   0.223    -.0692564    .2944177
             94  |   .0569003   .0938625     0.61   0.546    -.1289731    .2427737
             95  |   .0700592   .0853321     0.82   0.413    -.0989215      .23904
             96  |   .0763071   .1020234     0.75   0.456     -.125727    .2783412
             97  |   .0032573   .1373814     0.02   0.981    -.2687954    .2753099
             98  |   .0697331   .1006435     0.69   0.490    -.1295685    .2690347
             99  |  -.0226479   .0846387    -0.27   0.789    -.1902556    .1449598
            100  |   .0272374    .093552     0.29   0.771    -.1580209    .2124958
            101  |   .0071744   .0913672     0.08   0.938    -.1737575    .1881062
            102  |   -.000967   .1038971    -0.01   0.993    -.2067115    .2047776
            103  |  -.0061613   .0814355    -0.08   0.940    -.1674257    .1551031
            104  |   .0524127   .0873148     0.60   0.549    -.1204943    .2253198
            105  |   .0838599   .1047117     0.80   0.425    -.1234977    .2912176
            106  |     .06797   .1072802     0.63   0.528     -.144474     .280414
            107  |   .0677287   .1341325     0.50   0.615    -.1978903    .3333476
            108  |    .133141   .1051835     1.27   0.208    -.0751511     .341433
            109  |   .0354388    .111138     0.32   0.750    -.1846447    .2555224
            110  |   .1360479   .1172748     1.16   0.248     -.096188    .3682839
            111  |   .1887732   .1130257     1.67   0.098    -.0350485    .4125949
            112  |   .1531871   .1133764     1.35   0.179    -.0713291    .3777032
            113  |   .1606089   .1233376     1.30   0.195     -.083633    .4048509
            114  |    .231678    .112153     2.07   0.041     .0095845    .4537715
            115  |   .2179022   .1367173     1.59   0.114    -.0528352    .4886396
            116  |   .1841801   .1404581     1.31   0.192    -.0939651    .4623253
            117  |   .2593443   .1269119     2.04   0.043     .0080241    .5106644
            118  |    .191552   .1295918     1.48   0.142    -.0650751    .4481791
            119  |   .3418829   .1222609     2.80   0.006     .0997729    .5839928
            120  |   .2706548   .1344509     2.01   0.046     .0044054    .5369042
            121  |   .2949105   .1300078     2.27   0.025     .0374597    .5523613
            122  |   .2759598   .1361354     2.03   0.045     .0063748    .5455449
            123  |    .365766   .1274778     2.87   0.005     .1133253    .6182067
            124  |   .1964606   .1342924     1.46   0.146    -.0694749    .4623962
            125  |   .1733727   .1385654     1.25   0.213    -.1010246    .4477699
            126  |   .2674171   .1190133     2.25   0.027     .0317383    .5030959
            127  |   .1504222   .1129293     1.33   0.185    -.0732085    .3740529
            128  |   .2013811   .1138394     1.77   0.079    -.0240519    .4268141
            129  |   .1925026   .1241354     1.55   0.124    -.0533192    .4383245
            130  |   .1992236   .1209873     1.65   0.102    -.0403643    .4388115
            131  |   .1586782   .1302114     1.22   0.225    -.0991758    .4165322
            132  |   .2130356    .124794     1.71   0.090    -.0340904    .4601616
            133  |   .2338484    .129068     1.81   0.073    -.0217414    .4894382
            134  |   .2428987   .1344151     1.81   0.073    -.0232798    .5090772
            135  |   .2116095   .1208867     1.75   0.083    -.0277791    .4509981
            136  |   .2664201   .1259652     2.12   0.037     .0169746    .5158656
            137  |   .2507599   .1370196     1.83   0.070    -.0205761    .5220959
            138  |     .26064   .1418966     1.84   0.069    -.0203539    .5416338
            139  |    .312543   .1319993     2.37   0.020     .0511484    .5739376
            140  |   .3261879   .1332058     2.45   0.016     .0624042    .5899716
            141  |   .2716943   .1424145     1.91   0.059    -.0103251    .5537138
            142  |   .3470382    .132471     2.62   0.010     .0847096    .6093668
            143  |   .4823822   .1379796     3.50   0.001     .2091451    .7556193
            144  |   .3124669   .1494053     2.09   0.039     .0166038    .6083301
            145  |   .3040236   .1411083     2.15   0.033     .0245908    .5834564
            146  |   .3725079   .1396533     2.67   0.009     .0959563    .6490596
            147  |   .4073707   .1494441     2.73   0.007     .1114307    .7033106
            148  |    .373385   .1445251     2.58   0.011     .0871858    .6595841
            149  |   .3993425    .146637     2.72   0.007     .1089613    .6897238
            150  |   .4483316   .1464479     3.06   0.003     .1583248    .7383383
            151  |   .4623383   .1594198     2.90   0.004     .1466437    .7780329
            152  |   .4193369   .1532172     2.74   0.007     .1159251    .7227488
            153  |   .5061071   .1607929     3.15   0.002     .1876935    .8245208
            154  |   .4078365    .160625     2.54   0.012     .0897552    .7259178
            155  |   .5285764   .1658856     3.19   0.002     .2000777    .8570751
            156  |    .459087   .1571823     2.92   0.004     .1478232    .7703508
            157  |    .602488   .1847386     3.26   0.001     .2366553    .9683206
            158  |   .4693611   .1569211     2.99   0.003     .1586145    .7801076
            159  |   .5876982   .1653972     3.55   0.001     .2601668    .9152296
            160  |   .5207756   .1693356     3.08   0.003      .185445    .8561062
            161  |   .5022064   .1776411     2.83   0.006     .1504286    .8539841
            162  |    .539381   .1737443     3.10   0.002       .19532    .8834419
            163  |   .5072167   .1790784     2.83   0.005     .1525927    .8618407
            164  |   .4608808   .1651695     2.79   0.006     .1338003    .7879613
            165  |   .4455947   .1755366     2.54   0.012     .0979844     .793205
            166  |   .3788913   .1563461     2.42   0.017     .0692835    .6884991
            167  |   .4239653   .1587057     2.67   0.009     .1096848    .7382458
            168  |   .3904816   .1644056     2.38   0.019     .0649137    .7160494
            169  |   .4128752   .1702503     2.43   0.017     .0757333    .7500171
            170  |   .3603665     .16198     2.22   0.028      .039602    .6811311
            171  |   .4887751   .1669516     2.93   0.004     .1581655    .8193847
            172  |   .4118723   .1663738     2.48   0.015     .0824069    .7413378
            173  |   .2929846   .1730262     1.69   0.093    -.0496544    .6356236
            174  |   .3336438    .161609     2.06   0.041     .0136139    .6536737
            175  |   .3632253   .1871201     1.94   0.055    -.0073235    .7337741
            176  |   .3305094   .1843405     1.79   0.076     -.034535    .6955538
            177  |   .3699709   .1881072     1.97   0.052    -.0025325    .7424744
            178  |     .40748   .1630304     2.50   0.014     .0846354    .7303247
            179  |   .5148226    .176015     2.92   0.004      .166265    .8633802
            180  |   .5284791   .1617879     3.27   0.001     .2080951     .848863
            181  |   .4767073   .1905825     2.50   0.014      .099302    .8541125
            182  |   .5958493   .1786448     3.34   0.001      .242084    .9496146
            183  |    .628413   .1931406     3.25   0.001      .245942    1.010884
            184  |   .6689007   .1829918     3.66   0.000     .3065271    1.031274
            185  |   .5422775    .189944     2.85   0.005     .1661367    .9184182
            186  |   .6869088   .1818347     3.78   0.000     .3268266    1.046991
            187  |   .6781345    .204431     3.32   0.001     .2733055    1.082963
            188  |    .815548   .1936808     4.21   0.000     .4320073    1.199089
            189  |   .7831085   .2019487     3.88   0.000     .3831952    1.183022
            190  |   .7627859   .1741594     4.38   0.000      .417903    1.107669
            191  |   .7991028   .2115317     3.78   0.000     .3802125    1.217993
            192  |   .7879531   .2080489     3.79   0.000     .3759596    1.199947
            193  |   .6997222   .1785461     3.92   0.000     .3461524    1.053292
            194  |   .7038953    .204256     3.45   0.001     .2994127    1.108378
            195  |    .575429   .2371584     2.43   0.017     .1057908    1.045067
            196  |   .5109115   .1784477     2.86   0.005     .1575365    .8642866
            197  |   .4207373   .2252714     1.87   0.064    -.0253613     .866836
            198  |   .5255269   .1799654     2.92   0.004     .1691464    .8819074
            199  |    .578441    .188611     3.07   0.003     .2049399    .9519421
            200  |    .629176   .1941593     3.24   0.002     .2446877    1.013664
            201  |   .4439077   .1844492     2.41   0.018     .0786482    .8091673
            202  |   .5919404   .2030554     2.92   0.004     .1898354    .9940455
            203  |   .5761113   .2327998     2.47   0.015     .1151042    1.037118
            204  |   .6507395   .2083066     3.12   0.002     .2382357    1.063243
            205  |   .6264729   .1910615     3.28   0.001     .2481191    1.004827
            206  |   .5633758   .1819777     3.10   0.002     .2030104    .9237411
            207  |   .6579037   .2084198     3.16   0.002     .2451757    1.070632
            208  |   .7332402    .207785     3.53   0.001     .3217693    1.144711
            209  |    .561871   .1778738     3.16   0.002     .2096325    .9141096
            210  |   .6223397   .1981317     3.14   0.002      .229985    1.014694
            211  |   .5404157   .2274915     2.38   0.019     .0899207    .9909108
            212  |   .4646142   .1821058     2.55   0.012     .1039951    .8252332
            213  |    .491699   .1902489     2.58   0.011     .1149543    .8684437
            214  |   .5222486   .2130461     2.45   0.016     .1003593    .9441378
            215  |     .37337   .2714913     1.38   0.172    -.1642566    .9109967
            216  |   .1944305   .4967526     0.39   0.696    -.7892749    1.178136
            217  |   .6758979   .2383564     2.84   0.005     .2038874    1.147908
                 |
           _cons |  -.8194075   .1962445    -4.18   0.000    -1.208025   -.4307899
    -------------+----------------------------------------------------------------
         sigma_u |  .93503296
         sigma_e |  .63360056
             rho |  .68531924   (fraction of variance due to u_i)
    ------------------------------------------------------------------------------
    
    .
    end of do-file

  • #2
    Manni:
    see, -help j_robustsingular-.
    As an aside, you seem to have far too many predictors vs the number of your clusters.
    Kind regards,
    Carlo
    (Stata 16.0 SE)

    Comment


    • #3
      Thanks Carlo,
      I already had a look at the help file, but I cannot see why one of the mentioned points is the case in my estimation.
      As an explanation, the dataset is a panel on country-level ranging from 1975-2014. It contains quarterly data on country level. Since I want to control for time-fixed effects (as a kind of global shocks) I add dummies for each quarter. I thought this is a common approach and not of a bigger issue, but am grateful for any improvements or hints in this case.

      Regards,
      Manni

      Comment


      • #4
        As an aside, you seem to have far too many predictors vs the number of your clusters.
        Carlo was being too modest. That's not an aside--it's the answer to your question. You have only 119 groups, but you have far more variables in your regression than that. That leaves no degrees of freedom for a model-wide F statistic.

        Comment


        • #5
          Thanks, Clyde: very flattering!
          Kind regards,
          Carlo
          (Stata 16.0 SE)

          Comment


          • #6
            Thanks for your quick answer. My question is now, whether the coefficients and standard errors for the other explanatory variables are still unbiased or if I must reduce the number of variables to get valid estimations.

            Comment


            • #7
              The notion that linear regression produces unbiased estimates of model parameters if conditional on the model. If confounding variables are not included, then the parameter estimates will suffer from omitted variable bias. Inclusion of superfluous variables in the model will not introduce any bias in its own right, though it may well result in inflated standard errors.

              The coefficients of your time variables are actually larger, in general, than the coefficients for your other predictors. Without knowing the scale of the other variables, it is hard to compare all the coefficients. But if your other variables are also 0/1 variables, or continuous variables with a scale of order of magnitude 1, then the time variables actually dominate the regression model. So they could not, in that case, be considered superfluous. This suggests that you are looking for a rather weak signal in the presence of a lot of noise, but it does not argue for removal of any of these variables.

              If, on the other hand, your non-time variables have a large scale (say typical values are on the order of 100,000 to take a fairly extreme example), then even though they get multiplied by very small coefficients, they make very large contributions to the regression, and the time variables become negligible contributors. That would argue that the time variables are superfluous.

              So it may be worth your while to do a quick estimate of the scale of your variables and their relative contributions to the model.

              Anyway, to return to your question in #6, having more predictors than groups does not affect the unbiasedness (or not) of the coefficients conditional on the model, but what variables are included in the model can affect the validity of the model, and can have diverse effects on the standard errors. But in any case, the number of variables exceeding the number of groups and the undefinedness of the omnibus F-statistic have no direct bearing on these issues.

              Finally, I neglected to comment in my original post that the F test you are looking for is probably of no real use anyway. If it were defined, it would test the null hypothesis that all model coefficients are zero. Given that most of the model coefficients are time variables, included to control nuisance effects of idiosyncratic period disturbances, the hypothesis of all model coefficients being zero is probably pointless and of no interest to anybody. So you are no worse off for lack of that F statistic. The more interesting "omnibus" test would be the simultaneous test of elect_d, its lags and leads, and perhaps the four data* variables (if those are variables of interest in your analysis as opposed to also being thrown in just to adjust for nuisance effects). You have plenty of degrees of freedom available to run that F-test using the -test- command, and that one would be meaningful.

              Comment


              • #8
                Manni:
                you can investigate via:
                Code:
                testparm i.qdate
                whether it makes sense on not to include them among predictors.
                The following example may sound clearer:
                Code:
                . use "http://www.stata-press.com/data/r14/nlswork.dta", clear
                (National Longitudinal Survey.  Young Women 14-26 years of age in 1968)
                
                . xtreg ln_wage age i.race i.year, fe
                note: 2.race omitted because of collinearity
                note: 3.race omitted because of collinearity
                
                Fixed-effects (within) regression               Number of obs     =     28,510
                Group variable: idcode                          Number of groups  =      4,710
                
                R-sq:                                           Obs per group:
                     within  = 0.1060                                         min =          1
                     between = 0.0914                                         avg =        6.1
                     overall = 0.0805                                         max =         15
                
                                                                F(15,23785)       =     188.00
                corr(u_i, Xb)  = 0.0467                         Prob > F          =     0.0000
                
                ------------------------------------------------------------------------------
                     ln_wage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
                -------------+----------------------------------------------------------------
                         age |   .0125992   .0102163     1.23   0.217    -.0074253    .0326238
                             |
                        race |
                      black  |          0  (omitted)
                      other  |          0  (omitted)
                             |
                        year |
                         69  |   .0748621   .0159011     4.71   0.000      .043695    .1060292
                         70  |   .0478697   .0235673     2.03   0.042     .0016763     .094063
                         71  |   .0865577   .0327939     2.64   0.008     .0222795     .150836
                         72  |   .0856757   .0424903     2.02   0.044     .0023919    .1689594
                         73  |   .0880069    .052344     1.68   0.093    -.0145906    .1906044
                         75  |   .0778607   .0720304     1.08   0.280    -.0633235    .2190449
                         77  |    .108365   .0922272     1.17   0.240    -.0724063    .2891363
                         78  |   .1309518   .1028143     1.27   0.203    -.0705707    .3324743
                         80  |   .1142649    .122792     0.93   0.352    -.1264152     .354945
                         82  |   .1090451   .1431112     0.76   0.446    -.1714619    .3895522
                         83  |   .1211272   .1532018     0.79   0.429    -.1791581    .4214125
                         85  |   .1465637   .1736146     0.84   0.399    -.1937321    .4868594
                         87  |   .1382642   .1941163     0.71   0.476     -.242216    .5187445
                         88  |   .1799741   .2079871     0.87   0.387    -.2276938     .587642
                             |
                       _cons |   1.203731   .1952306     6.17   0.000     .8210667    1.586396
                -------------+----------------------------------------------------------------
                     sigma_u |   .4058746
                     sigma_e |  .30300411
                         rho |  .64212421   (fraction of variance due to u_i)
                ------------------------------------------------------------------------------
                F test that all u_i=0: F(4709, 23785) = 8.80                 Prob > F = 0.0000
                
                . testparm i.year
                
                 ( 1)  69.year = 0
                 ( 2)  70.year = 0
                 ( 3)  71.year = 0
                 ( 4)  72.year = 0
                 ( 5)  73.year = 0
                 ( 6)  75.year = 0
                 ( 7)  77.year = 0
                 ( 8)  78.year = 0
                 ( 9)  80.year = 0
                 (10)  82.year = 0
                 (11)  83.year = 0
                 (12)  85.year = 0
                 (13)  87.year = 0
                 (14)  88.year = 0
                
                       F( 14, 23785) =    6.50
                            Prob > F =    0.0000
                In the example -testparm- results support rejecting the null that, taken years together, the their coefficient equals 0.
                Last edited by Carlo Lazzaro; 22 Jan 2017, 02:37.
                Kind regards,
                Carlo
                (Stata 16.0 SE)

                Comment


                • #9
                  Yes, except that it seems to me that Manni has something like 150 time variables in his model. With only 119 groups, that F test will fail for the same reason that the omnibus F test failed: exhausted degrees of freedom.

                  Comment


                  • #10
                    Clyde:
                    yes, I suspect the same; I am only curious about the result.
                    Kind regards,
                    Carlo
                    (Stata 16.0 SE)

                    Comment


                    • #11
                      Thanks to both of you for your detailled and very helpful answers!
                      The basic idea of the regression is to see wether elections (coded as dummy==1 in the respective quarters) do influence capital flows (the dependent variable). The four data* variables are simply controls like GDP p.c. or population to account for other factors determining capital flows --> Thanks Clyde for pointing to the differences in size of coefficients, but as the variables have large scale (e.g. population) this should not be a problem as you already mentioned.
                      The reason for using so many time controls is that I want to rule out that the effects of global shocks are mixed with election induced changes of capital flows within a country. Furthermore I can only control for changes within the receipient country of capital flows and not for the countries abroad. But if I understood you correctly, it is possible to include quarterly time-variables to the regression without issues for my explanatory election dummies.
                      Furthermore, I did the proposed testparm. It worked out with the following results:

                      Code:
                      F(118,   118) = 5416.65
                      Prob > F =    0.0000
                      But stated that several constraints have been dropped. I furthermore conducted testparm for splitted parts of the sample (by time periods) and always found the H_0 rejected.
                      Please correct me if I'm wrong, but I think I should keep the time-dummies.

                      Regards,
                      Manni

                      Comment


                      • #12
                        Manni:
                        you're welcome, but the main issue remains the same: you have far too many predictors in your regression model.
                        From a practical standpoint (that is, set aside for a while any remark on the degrees of freedom), I would have hard times in disentagling the contribution of your predictors in explaining the variation of the -depvar- in a paper to be submitted to any decent peer-reviewed journal.
                        Kind regards,
                        Carlo
                        (Stata 16.0 SE)

                        Comment


                        • #13
                          My work is quite related to the recently published paper of Julio & Yook: "Policy uncertainty, irreversibility, and cross-border flows of capital" (Journal of International Economics, 2016). The authors have a panel dataset including the period 1994-2010 and 43 countries. They use similar controls like me and additionally use country-fixed effects as well as quarterly time-fixed-effects. Furthermore, they declare their SE to be clustered at country level. I think that this should be quite similar to my approach. Hence, to my understanding they should have the same issues concerning degrees of freedom mentioned by Clyde above - or do I miss something important what makes their regression different from mine?

                          Regards,
                          Manni

                          Comment


                          • #14
                            Manni:
                            one approach would be to post the corresponding author directly and ask for clarifications.
                            Kind regards,
                            Carlo
                            (Stata 16.0 SE)

                            Comment


                            • #15
                              I'm going to disagree with Carlo's response in #12. You can certainly test the 5 elect* variables, individually and jointly, in your model, and those tests are valid, even though you can't do an omnibus test on the entire model. Too many variables, even when it doesn't exhaust the degrees of freedom, can cause overfitting problems, but in your case we are talking about ~300 explicit variables (+ 119 fixed effects) in ~9,600 observations (regardless of # of groups). While I wouldn't call that a generous observations to variables ratio, it's not so small as to be a serious problem. If everything except those 5 variables is there just to adjust for nuisance variation, I think this is quite acceptable.

                              Comment

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