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  • #1

    When my FE and RE models (xtngreg) fail to converge_Continued

    How are you?

    I am sorry to post the same topic twice no the forum.

    Is there anyone who can let me know why my FE and RE models fail to converge?

    For your information, I am using a count dependent variable (the number of homicide on a given day in a given state and the conditional variance is greater than the conditional mean. And, the total number of the independent variables is about 196. So, I run xtnbreg in Stata 14.1. I think that some categorical variables should be dropped from the analysis. Am I right?

    Code:
    xtset REF_STATE REF_TIME
       panel variable:  REF_STATE (strongly balanced)
        time variable:  REF_TIME, 01 Jan 79 to 31 Dec 98
                delta:  1 day
    This is my FE negative binomial regression model with iterations of 20.

    Code:
     xtnbreg HOMICIDE_COUNTS E_B_14-E_B_1 EXECUTE_DATE E_A_1-E_A_14 NEWS_B_14-NEWS_B_1 NEWS_ON_THE_GIVEN_EXECUTION NEWS_A_1-NEWS_A_14 TV_B_14-TV_B_1 TV_ON_THE_GIVEN_EXECUTION TV_A_1-TV_A_14 ACTIVE_DEATH_PENALTY DUMMY79 DUMMY80 DUMMY81 DUMMY82 DUMMY83 DUMMY84 DUMMY85 DUMMY86 DUMMY88 DUMMY89 DUMMY90 DUMMY91 DUMMY92 DUMMY93 DUMMY94 DUMMY95 DUMMY96 DUMMY97 DUMMY98 jan feb mar may jun jul aug sep oct nov dec SUN_DUMMY MON_DUMMY TUE_DUMMY WED_DUMMY THUR_DUMMY FRI_DUMMY SAT_DUMMY dummy_29 newyear NEW_YEAR_LAG_1 goodfriday Easter_LAG_1 memorial_Day MEMORIAL_LAG_1 independence_Day INDEPENDENCE_LAG_1 labor_day LABOR_LAG_1 thanksgiving THANKSGIVING_LAG_1 christmas CHRISTMAS_LAG_1 STATE_RESIDENTS STATE_PRISONERS STATE_PRISONER_RATE alabama alaska arizona arkansas colorado connecticut delaware washington_dc florida georgia hawaii idaho illinois indiana iowa kansas kentucky louisiana maine maryland massachusetts michigan minnesota mississippi missouri montana nebraska nevada new_hampshire new_jersey new_mexico new_york north_carolina north_dakota ohio oklahoma oregon pennsylvania rhode_island south_carolina south_dakota tennessee texas utah virginia vermont washington west_virginia wisconsin wyoming, fe
 
note: TV_B_14 omitted because of collinearity
note: TV_B_13 omitted because of collinearity
note: TV_B_12 omitted because of collinearity
note: TV_B_11 omitted because of collinearity
note: TV_B_10 omitted because of collinearity
note: TV_B_9 omitted because of collinearity
note: TV_B_8 omitted because of collinearity
note: TV_B_7 omitted because of collinearity
note: TV_A_4 omitted because of collinearity
note: TV_A_5 omitted because of collinearity
note: TV_A_6 omitted because of collinearity
note: TV_A_7 omitted because of collinearity
note: TV_A_8 omitted because of collinearity
note: TV_A_9 omitted because of collinearity
note: TV_A_10 omitted because of collinearity
note: TV_A_11 omitted because of collinearity
note: TV_A_12 omitted because of collinearity
note: TV_A_13 omitted because of collinearity
note: TV_A_14 omitted because of collinearity
note: SAT_DUMMY omitted because of collinearity
 
Iteration 0:   log likelihood = -415987.64
Iteration 1:   log likelihood = -404760.13  (not concave)
Iteration 2:   log likelihood = -403248.28  (not concave)
Iteration 3:   log likelihood =  -403145.1  (not concave)
Iteration 4:   log likelihood = -403134.91  (not concave)
Iteration 5:   log likelihood = -403134.91  (not concave)
Iteration 6:   log likelihood =  -403134.9  (not concave)
Iteration 7:   log likelihood = -403134.83  (not concave)
Iteration 8:   log likelihood = -403134.77  (not concave)
Iteration 9:   log likelihood = -403134.76  (not concave)
Iteration 10:  log likelihood = -403134.76  (not concave)
Iteration 11:  log likelihood =  -403134.7  (not concave)
Iteration 12:  log likelihood = -403134.63  (not concave)
Iteration 13:  log likelihood = -403134.63  (not concave)
Iteration 14:  log likelihood = -403134.62  (not concave)
Iteration 15:  log likelihood = -403134.62  (not concave)
Iteration 16:  log likelihood = -403134.62  (not concave)
Iteration 17:  log likelihood = -403134.61  (not concave)
Iteration 18:  log likelihood = -403134.61  (not concave)
Iteration 19:  log likelihood =  -403134.6  (not concave)
Iteration 20:  log likelihood =  -403134.6  (not concave)
convergence not achieved
 
Conditional FE negative binomial regression     Number of obs     =    372,526
Group variable: REF_STATE                       Number of groups  =         51
 
                                                Obs per group:
                                                              min =      7,291
                                                              avg =    7,304.4
                                                              max =      7,305
 
                                                Wald chi2(171)    =          .
Log likelihood  =  -403134.6                    Prob > chi2       =          .
 
---------------------------------------------------------------------------------------------
            HOMICIDE_COUNTS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
                     E_B_14 |    .072223   .0288578     2.50   0.012     .0156627    .1287834
                     E_B_13 |   .0331979   .0286412     1.16   0.246    -.0229377    .0893336
                     E_B_12 |   .0063371   .0282129     0.22   0.822    -.0489592    .0616334
                     E_B_11 |   .0086962   .0278666     0.31   0.755    -.0459214    .0633138
                     E_B_10 |  -.0089882   .0279721    -0.32   0.748    -.0638126    .0458362
                      E_B_9 |   .0377917   .0281933     1.34   0.180    -.0174663    .0930496
                      E_B_8 |   .0993117   .0283375     3.50   0.000     .0437713    .1548522
                      E_B_7 |   .0803985   .0287379     2.80   0.005     .0240733    .1367237
                      E_B_6 |   .0672748    .028163     2.39   0.017     .0120764    .1224732
                      E_B_5 |   .0369834   .0279608     1.32   0.186    -.0178188    .0917855
                      E_B_4 |  -.0186257   .0282228    -0.66   0.509    -.0739414    .0366899
                      E_B_3 |  -.0188527   .0282556    -0.67   0.505    -.0742327    .0365273
                      E_B_2 |    .018677   .0286623     0.65   0.515    -.0375002    .0748541
                      E_B_1 |   .0610474   .0292132     2.09   0.037     .0037905    .1183042
               EXECUTE_DATE |   .0384437   .0300349     1.28   0.201    -.0204237    .0973111
                      E_A_1 |   .0584959   .0292158     2.00   0.045      .001234    .1157579
                      E_A_2 |   .0409928   .0279566     1.47   0.143    -.0138012    .0957868
                      E_A_3 |  -.0088616   .0281997    -0.31   0.753     -.064132    .0464088
                      E_A_4 |  -.0316388   .0283189    -1.12   0.264    -.0871429    .0238652
                      E_A_5 |  -.0322646   .0290027    -1.11   0.266    -.0891089    .0245797
                      E_A_6 |   .0346549   .0290504     1.19   0.233    -.0222828    .0915926
                      E_A_7 |   .0598607   .0288372     2.08   0.038     .0033408    .1163806
                      E_A_8 |   .0863557   .0278774     3.10   0.002      .031717    .1409944
                      E_A_9 |   .0363015   .0279029     1.30   0.193    -.0183872    .0909901
                     E_A_10 |   .0091589   .0277896     0.33   0.742    -.0453076    .0636255
                     E_A_11 |  -.0027468   .0277989    -0.10   0.921    -.0572316     .051738
                     E_A_12 |   .0430316   .0280677     1.53   0.125      -.01198    .0980433
                     E_A_13 |   .0442301   .0289159     1.53   0.126    -.0124441    .1009043
                     E_A_14 |   .0431949   .0290302     1.49   0.137    -.0137033    .1000932
                  NEWS_B_14 |  -.1255949   .0813264    -1.54   0.123    -.2849918     .033802
                  NEWS_B_13 |    .103043   .0449008     2.29   0.022     .0150391    .1910469
                  NEWS_B_12 |    -.00601   .0425012    -0.14   0.888    -.0893108    .0772908
                  NEWS_B_11 |  -.0230489    .032052    -0.72   0.472    -.0858696    .0397718
                  NEWS_B_10 |  -.0011994   .0478999    -0.03   0.980    -.0950814    .0926826
                   NEWS_B_9 |   .0419296   .0272763     1.54   0.124     -.011531    .0953901
                   NEWS_B_8 |   .0155875   .0261562     0.60   0.551    -.0356776    .0668527
                   NEWS_B_7 |   .0092613   .0275496     0.34   0.737     -.044735    .0632575
                   NEWS_B_6 |   .0084543   .0208371     0.41   0.685    -.0323857    .0492943
                   NEWS_B_5 |   .0071559   .0211163     0.34   0.735    -.0342313    .0485431
                   NEWS_B_4 |   .0057686   .0164141     0.35   0.725    -.0264024    .0379397
                   NEWS_B_3 |   .0187828   .0231916     0.81   0.418    -.0266718    .0642375
                   NEWS_B_2 |  -.0079807   .0152442    -0.52   0.601    -.0378588    .0218975
                   NEWS_B_1 |   .0051763   .0095923     0.54   0.589    -.0136243    .0239768
NEWS_ON_THE_GIVEN_EXECUTION |   .0077566   .0061186     1.27   0.205    -.0042355    .0197488
                   NEWS_A_1 |   -.001856   .0037016    -0.50   0.616     -.009111    .0053991
                   NEWS_A_2 |   .0004008   .0096108     0.04   0.967    -.0184361    .0192376
                   NEWS_A_3 |  -.0334815   .0183096    -1.83   0.067    -.0693677    .0024047
                   NEWS_A_4 |  -.0217785   .0227728    -0.96   0.339    -.0664123    .0228553
                   NEWS_A_5 |  -.0073113   .0218193    -0.34   0.738    -.0500763    .0354537
                   NEWS_A_6 |   .0458547   .0442958     1.04   0.301    -.0409635    .1326729
                   NEWS_A_7 |   .0340186   .0572131     0.59   0.552     -.078117    .1461542
                   NEWS_A_8 |   .0533057   .0759449     0.70   0.483    -.0955436    .2021549
                   NEWS_A_9 |  -.0184076   .0590349    -0.31   0.755    -.1341139    .0972987
                  NEWS_A_10 |   .1819983   .0637407     2.86   0.004     .0570687    .3069278
                  NEWS_A_11 |   .0905438   .0702083     1.29   0.197     -.047062    .2281496
                  NEWS_A_12 |  -.0925455   .0724286    -1.28   0.201     -.234503    .0494119
                  NEWS_A_13 |   .2519032   .2413479     1.04   0.297      -.22113    .7249364
                  NEWS_A_14 |   .1477908   .1103132     1.34   0.180    -.0684191    .3640006
                    TV_B_14 |          0  (omitted)
                    TV_B_13 |          0  (omitted)
                    TV_B_12 |          0  (omitted)
                    TV_B_11 |          0  (omitted)
                    TV_B_10 |          0  (omitted)
                     TV_B_9 |          0  (omitted)
                     TV_B_8 |          0  (omitted)
                     TV_B_7 |          0  (omitted)
                     TV_B_6 |  -.0089164   .0821862    -0.11   0.914    -.1699985    .1521657
                     TV_B_5 |   .0433135   .0687069     0.63   0.528    -.0913494    .1779765
                     TV_B_4 |  -.1842698    .145718    -1.26   0.206    -.4698718    .1013321
                     TV_B_3 |   -.038108   .0496794    -0.77   0.443    -.1354778    .0592618
                     TV_B_2 |  -.0241972   .0244757    -0.99   0.323    -.0721688    .0237743
                     TV_B_1 |   .0178567   .0133769     1.33   0.182    -.0083616     .044075
  TV_ON_THE_GIVEN_EXECUTION |  -.0069243     .01088    -0.64   0.524    -.0282488    .0144001
                     TV_A_1 |  -.0120575   .0332839    -0.36   0.717    -.0772927    .0531777
                     TV_A_2 |   .0898798   .0976012     0.92   0.357     -.101415    .2811747
                     TV_A_3 |   .0180684   .0490214     0.37   0.712    -.0780119    .1141487
                     TV_A_4 |          0  (omitted)
                     TV_A_5 |          0  (omitted)
                     TV_A_6 |          0  (omitted)
                     TV_A_7 |          0  (omitted)
                     TV_A_8 |          0  (omitted)
                     TV_A_9 |          0  (omitted)
                    TV_A_10 |          0  (omitted)
                    TV_A_11 |          0  (omitted)
                    TV_A_12 |          0  (omitted)
                    TV_A_13 |          0  (omitted)
                    TV_A_14 |          0  (omitted)
       ACTIVE_DEATH_PENALTY |  -.2547375   .0137387   -18.54   0.000    -.2816648   -.2278102
                    DUMMY79 |   .1636361   .0108769    15.04   0.000     .1423178    .1849544
                    DUMMY80 |   .2172006   .0105827    20.52   0.000     .1964589    .2379423
                    DUMMY81 |   .1685799   .0105721    15.95   0.000      .147859    .1893008
                    DUMMY82 |   .0902207   .0106398     8.48   0.000      .069367    .1110743
                    DUMMY83 |  -.0195425   .0107789    -1.81   0.070    -.0406686    .0015837
                    DUMMY84 |  -.0448863   .0107566    -4.17   0.000    -.0659689   -.0238037
                    DUMMY85 |  -.0478786    .010707    -4.47   0.000     -.068864   -.0268932
                    DUMMY86 |   .0378865   .0104169     3.64   0.000     .0174698    .0583033
                    DUMMY88 |    .029456   .0104153     2.83   0.005     .0090425    .0498696
                    DUMMY89 |   .0651112   .0103836     6.27   0.000     .0447597    .0854626
                    DUMMY90 |   .1546628   .0103012    15.01   0.000     .1344727    .1748529
                    DUMMY91 |   .2259401   .0101943    22.16   0.000     .2059596    .2459206
                    DUMMY92 |   .1841083   .0104698    17.58   0.000      .163588    .2046287
                    DUMMY93 |    .217696   .0104974    20.74   0.000     .1971214    .2382705
                    DUMMY94 |   .1891101   .0108283    17.46   0.000      .167887    .2103332
                    DUMMY95 |   .1337645   .0113826    11.75   0.000      .111455    .1560741
                    DUMMY96 |   .0719522   .0118399     6.08   0.000     .0487465    .0951579
                    DUMMY97 |   .0350241   .0120496     2.91   0.004     .0114074    .0586409
                    DUMMY98 |  -.0671233   .0124616    -5.39   0.000    -.0915475    -.042699
                        jan |   .0113167   .0082611     1.37   0.171    -.0048747    .0275082
                        feb |    .020319   .0082874     2.45   0.014     .0040759     .036562
                        mar |  -.0004254   .0081052    -0.05   0.958    -.0163114    .0154606
                        may |   .0144807   .0082033     1.77   0.078    -.0015976     .030559
                        jun |   .0549977   .0081008     6.79   0.000     .0391203     .070875
                        jul |   .1059114   .0080737    13.12   0.000     .0900874    .1217355
                        aug |   .1300949   .0078909    16.49   0.000      .114629    .1455608
                        sep |   .0808598   .0081761     9.89   0.000      .064835    .0968846
                        oct |   .0592032   .0080383     7.37   0.000     .0434485    .0749579
                        nov |   .0279143   .0082914     3.37   0.001     .0116635     .044165
                        dec |   .0422234   .0082057     5.15   0.000     .0261405    .0583064
                  SUN_DUMMY |  -.0786056   .0054836   -14.33   0.000    -.0893533    -.067858
                  MON_DUMMY |   -.312059   .0059598   -52.36   0.000      -.32374    -.300378
                  TUE_DUMMY |  -.3520316   .0060419   -58.26   0.000    -.3638736   -.3401896
                  WED_DUMMY |  -.3678087   .0059989   -61.31   0.000    -.3795663    -.356051
                 THUR_DUMMY |  -.3505068   .0059844   -58.57   0.000    -.3622361   -.3387775
                  FRI_DUMMY |  -.2409475   .0057944   -41.58   0.000    -.2523043   -.2295908
                  SAT_DUMMY |          0  (omitted)
                   dummy_29 |  -.0416922   .0110366    -3.78   0.000    -.0633234   -.0200609
                    newyear |   .6113558   .0239908    25.48   0.000     .5643347     .658377
             NEW_YEAR_LAG_1 |   .0266272   .0313503     0.85   0.396    -.0348183    .0880728
                 goodfriday |   .0743427   .0320272     2.32   0.020     .0115705    .1371149
               Easter_LAG_1 |  -.0366815   .0339647    -1.08   0.280     -.103251     .029888
               memorial_Day |   .0406985   .0324887     1.25   0.210    -.0229782    .1043751
             MEMORIAL_LAG_1 |   .0932023    .032059     2.91   0.004     .0303679    .1560367
           independence_Day |   .2009106   .0273901     7.34   0.000      .147227    .2545942
         INDEPENDENCE_LAG_1 |    .043118   .0295209     1.46   0.144    -.0147419    .1009779
                  labor_day |   .1659353   .0296495     5.60   0.000     .1078234    .2240473
                LABOR_LAG_1 |   .0996246   .0311932     3.19   0.001      .038487    .1607622
               thanksgiving |   .1699348   .0309258     5.49   0.000     .1093213    .2305484
         THANKSGIVING_LAG_1 |   .0739842   .0306576     2.41   0.016     .0138964     .134072
                  christmas |   .1601114   .0290553     5.51   0.000      .103164    .2170588
            CHRISTMAS_LAG_1 |  -.0917426   .0324239    -2.83   0.005    -.1552922    -.028193
            STATE_RESIDENTS |   1.06e-07   3.31e-09    31.95   0.000     9.93e-08    1.12e-07
            STATE_PRISONERS |  -9.17e-06   2.49e-07   -36.86   0.000    -9.66e-06   -8.69e-06
        STATE_PRISONER_RATE |   .0005794   .0000316    18.36   0.000     .0005175    .0006412
                    alabama |  -4.378142   .2501127   -17.50   0.000    -4.868354    -3.88793
                     alaska |  -7.581555   .2461227   -30.80   0.000    -8.063946   -7.099163
                    arizona |  -4.872936   .2480146   -19.65   0.000    -5.359035   -4.386836
                   arkansas |  -5.300156   .2392963   -22.15   0.000    -5.769168   -4.831144
                   colorado |  -5.597914   .2383725   -23.48   0.000    -6.065115   -5.130712
                connecticut |  -5.928583   .2373001   -24.98   0.000    -6.393682   -5.463483
                   delaware |   -7.44167   .2559057   -29.08   0.000    -7.943236   -6.940104
              washington_dc |  -5.127385   .2637477   -19.44   0.000    -5.644321   -4.610449
                    florida |  -4.291019   .2467389   -17.39   0.000    -4.774618   -3.807419
                    georgia |   -4.30965   .2422654   -17.79   0.000    -4.784482   -3.834819
                     hawaii |  -7.459021   .2464075   -30.27   0.000    -7.941971   -6.976071
                      idaho |  -7.604584   .2521814   -30.16   0.000    -8.098851   -7.110318
                   illinois |  -4.356974   .2443986   -17.83   0.000    -4.835987   -3.877962
                    indiana |   -4.73288   .2576214   -18.37   0.000    -5.237809   -4.227951
                       iowa |   -7.26359     .24276   -29.92   0.000     -7.73939   -6.787789
                     kansas |  -6.385772   .2348415   -27.19   0.000    -6.846052   -5.925491
                   kentucky |  -5.222458   .2428681   -21.50   0.000    -5.698471   -4.746445
                  louisiana |  -4.305524   .2405232   -17.90   0.000     -4.77694   -3.834107
                      maine |  -7.930229   .2601815   -30.48   0.000    -8.440175   -7.420283
                   maryland |  -4.459367   .2530153   -17.62   0.000    -4.955268   -3.963467
              massachusetts |  -5.709914   .2461843   -23.19   0.000    -6.192426   -5.227402
                   michigan |  -4.632073   .2413985   -19.19   0.000    -5.105205   -4.158941
                  minnesota |  -6.519566   .2386626   -27.32   0.000    -6.987336   -6.051796
                mississippi |  -4.696687   .2432474   -19.31   0.000    -5.173443   -4.219931
                   missouri |  -4.420423   .2546286   -17.36   0.000    -4.919485    -3.92136
                    montana |  -7.378037   .2510604   -29.39   0.000    -7.870107   -6.885968
                   nebraska |  -7.066626   .2442784   -28.93   0.000    -7.545403   -6.587849
                     nevada |  -6.046191   .2353813   -25.69   0.000     -6.50753   -5.584852
              new_hampshire |  -8.094007   .2672674   -30.28   0.000    -8.617841   -7.570172
                 new_jersey |  -4.675191   .2741257   -17.05   0.000    -5.212468   -4.137915
                 new_mexico |  -5.896493   .2338258   -25.22   0.000    -6.354783   -5.438203
                   new_york |  -4.145537   .4761172    -8.71   0.000    -5.078709   -3.212364
             north_carolina |  -4.295052   .2482794   -17.30   0.000    -4.781671   -3.808433
               north_dakota |   -8.58617   .2921077   -29.39   0.000    -9.158691    -8.01365
                       ohio |  -4.372529    .287015   -15.23   0.000    -4.935068    -3.80999
                   oklahoma |  -5.399021   .2340853   -23.06   0.000     -5.85782   -4.940222
                     oregon |  -6.140218   .2364159   -25.97   0.000    -6.603585   -5.676852
               pennsylvania |  -4.340099   .2988335   -14.52   0.000    -4.925801   -3.754396
               rhode_island |  -7.469178   .2499766   -29.88   0.000    -7.959124   -6.979233
             south_carolina |  -4.713285   .2489907   -18.93   0.000    -5.201298   -4.225272
               south_dakota |  -7.808735   .2730177   -28.60   0.000     -8.34384    -7.27363
                  tennessee |  -4.276465   .2500204   -17.10   0.000    -4.766496   -3.786434
                      texas |  -3.468085          .        .       .            .           .
                       utah |  -6.888754   .2428246   -28.37   0.000    -7.364682   -6.412827
                   virginia |  -4.429502   .2608205   -16.98   0.000    -4.940701   -3.918303
                    vermont |  -8.438065          .        .       .            .           .
                 washington |  -5.385913   .2446894   -22.01   0.000    -5.865495   -4.906331
              west_virginia |   -6.59443   .2341485   -28.16   0.000    -7.053353   -6.135507
                  wisconsin |  -5.960288   .2411684   -24.71   0.000    -6.432969   -5.487606
                    wyoming |  -8.018067   .2681818   -29.90   0.000    -8.543693    -7.49244
                      _cons |   6.374196   .2202872    28.94   0.000     5.942441    6.805951
---------------------------------------------------------------------------------------------
convergence not achieved
r(430);
 
. estimates store fixed
    Last edited by Moonki Hong; 01 Nov 2015, 00:22.
    Tags: None
  • #2
    I add my RE negative binomial regression model here because of the data limitation. This is the RE negative binomial regression model with the same condition as the FE model (20 iterations).

    Code:
    . xtnbreg HOMICIDE_COUNTS E_B_14-E_B_1 EXECUTE_DATE E_A_1-E_A_14 NEWS_B_14-NEWS_B_1 NEWS_ON_THE_GIVEN_EXECUTION NEWS_A_1-NEWS_A_14 TV_B_14-TV_B_1 TV_ON_THE_GIVEN_EXECUTION TV_A_1-TV_A_14 ACTIVE_DEATH_PENALTY DUMMY79 DUMMY80 DUMMY81 DUMMY82 DUMMY83 DUMMY84 DUMMY85 DUMMY86 DUMMY88 DUMMY89 DUMMY90 DUMMY91 DUMMY92 DUMMY93 DUMMY94 DUMMY95 DUMMY96 DUMMY97 DUMMY98 jan feb mar may jun jul aug sep oct nov dec SUN_DUMMY MON_DUMMY TUE_DUMMY WED_DUMMY THUR_DUMMY FRI_DUMMY SAT_DUMMY dummy_29 newyear NEW_YEAR_LAG_1 goodfriday Easter_LAG_1 memorial_Day MEMORIAL_LAG_1 independence_Day INDEPENDENCE_LAG_1 labor_day LABOR_LAG_1 thanksgiving THANKSGIVING_LAG_1 christmas CHRISTMAS_LAG_1 STATE_RESIDENTS STATE_PRISONERS STATE_PRISONER_RATE alabama alaska arizona arkansas colorado connecticut delaware washington_dc florida georgia hawaii idaho illinois indiana iowa kansas kentucky louisiana maine maryland massachusetts michigan minnesota mississippi missouri montana nebraska nevada new_hampshire new_jersey new_mexico new_york north_carolina north_dakota ohio oklahoma oregon pennsylvania rhode_island south_carolina south_dakota tennessee texas utah virginia vermont washington west_virginia wisconsin wyoming, re
note: TV_B_14 omitted because of collinearity
note: TV_B_13 omitted because of collinearity
note: TV_B_12 omitted because of collinearity
note: TV_B_11 omitted because of collinearity
note: TV_B_10 omitted because of collinearity
note: TV_B_9 omitted because of collinearity
note: TV_B_8 omitted because of collinearity
note: TV_B_7 omitted because of collinearity
note: TV_A_4 omitted because of collinearity
note: TV_A_5 omitted because of collinearity
note: TV_A_6 omitted because of collinearity
note: TV_A_7 omitted because of collinearity
note: TV_A_8 omitted because of collinearity
note: TV_A_9 omitted because of collinearity
note: TV_A_10 omitted because of collinearity
note: TV_A_11 omitted because of collinearity
note: TV_A_12 omitted because of collinearity
note: TV_A_13 omitted because of collinearity
note: TV_A_14 omitted because of collinearity
note: SAT_DUMMY omitted because of collinearity
 
Fitting negative binomial (constant dispersion) model:
 
Iteration 0:   log likelihood = -1048587.7 
Iteration 1:   log likelihood = -604949.63 
Iteration 2:   log likelihood = -423717.94 
Iteration 3:   log likelihood =  -405236.8 
Iteration 4:   log likelihood = -405072.23 
Iteration 5:   log likelihood = -405072.09 
Iteration 6:   log likelihood = -405072.09 
 
Iteration 0:   log likelihood = -569992.65 
Iteration 1:   log likelihood = -556633.53 
Iteration 2:   log likelihood = -551810.07 
Iteration 3:   log likelihood =    -551806 
Iteration 4:   log likelihood =    -551806 
 
Iteration 0:   log likelihood =    -551806  (not concave)
Iteration 1:   log likelihood = -541876.92  (not concave)
Iteration 2:   log likelihood =  -521819.3  (not concave)
Iteration 3:   log likelihood = -471930.04 
Iteration 4:   log likelihood = -445570.14 
Iteration 5:   log likelihood = -406550.45 
Iteration 6:   log likelihood =  -403001.2 
Iteration 7:   log likelihood = -402826.25 
Iteration 8:   log likelihood =  -402824.7 
Iteration 9:   log likelihood =  -402824.7 
 
Fitting full model:
 
Iteration 0:   log likelihood = -417723.63 
Iteration 1:   log likelihood = -405332.41  (not concave)
Iteration 2:   log likelihood = -405277.48  (not concave)
Iteration 3:   log likelihood = -405275.79  (not concave)
Iteration 4:   log likelihood = -405275.43  (not concave)
Iteration 5:   log likelihood = -405275.34  (not concave)
Iteration 6:   log likelihood = -405275.28  (not concave)
Iteration 7:   log likelihood = -405275.24  (not concave)
Iteration 8:   log likelihood = -405275.19  (not concave)
Iteration 9:   log likelihood = -405275.15  (not concave)
Iteration 10:  log likelihood = -405275.11  (not concave)
Iteration 11:  log likelihood = -405275.04  (not concave)
Iteration 12:  log likelihood = -405274.98  (not concave)
Iteration 13:  log likelihood = -405274.94  (not concave)
Iteration 14:  log likelihood = -405274.89  (not concave)
Iteration 15:  log likelihood = -405267.22  (not concave)
Iteration 16:  log likelihood = -405257.77  (not concave)
Iteration 17:  log likelihood = -405257.23  (not concave)
Iteration 18:  log likelihood =  -405257.2  (not concave)
Iteration 19:  log likelihood = -405257.15  (not concave)
Iteration 20:  log likelihood = -405257.08  (not concave)
convergence not achieved
 
Random-effects negative binomial regression     Number of obs     =    372,526
Group variable: REF_STATE                       Number of groups  =         51
 
Random effects u_i ~ Beta                       Obs per group:
                                                              min =      7,291
                                                              avg =    7,304.4
                                                              max =      7,305
 
                                                Wald chi2(173)    =   53168.28
Log likelihood  = -405257.08                    Prob > chi2       =     0.0000
 
---------------------------------------------------------------------------------------------
            HOMICIDE_COUNTS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
                     E_B_14 |  -.0301296   .0332441    -0.91   0.365    -.0952868    .0350277
                     E_B_13 |  -.0609297   .0327889    -1.86   0.063    -.1251949    .0033354
                     E_B_12 |  -.0513786   .0316151    -1.63   0.104     -.113343    .0105858
                     E_B_11 |   .0023453   .0303951     0.08   0.938     -.057228    .0619185
                     E_B_10 |  -.0195513   .0305847    -0.64   0.523    -.0794962    .0403936
                      E_B_9 |    .005778    .031348     0.18   0.854     -.055663    .0672189
                      E_B_8 |   .0091058   .0325403     0.28   0.780     -.054672    .0728837
                      E_B_7 |  -.0031675   .0328725    -0.10   0.923    -.0675963    .0612614
                      E_B_6 |  -.0030194   .0319568    -0.09   0.925    -.0656535    .0596147
                      E_B_5 |   .0010235   .0310128     0.03   0.974    -.0597604    .0618074
                      E_B_4 |  -.0571397   .0312588    -1.83   0.068    -.1184059    .0041264
                      E_B_3 |  -.0217178   .0307655    -0.71   0.480    -.0820171    .0385814
                      E_B_2 |    -.03336   .0322125    -1.04   0.300    -.0964954    .0297754
                      E_B_1 |  -.0469496   .0338487    -1.39   0.165    -.1132918    .0193926
               EXECUTE_DATE |  -.0597301   .0345491    -1.73   0.084    -.1274451    .0079848
                      E_A_1 |  -.0267642   .0331904    -0.81   0.420    -.0918161    .0382878
                      E_A_2 |  -.0117535   .0312934    -0.38   0.707    -.0730874    .0495805
                      E_A_3 |  -.0300562   .0309937    -0.97   0.332    -.0908028    .0306903
                      E_A_4 |  -.0399618   .0309487    -1.29   0.197    -.1006201    .0206965
                      E_A_5 |  -.1100224   .0329933    -3.33   0.001    -.1746881   -.0453566
                      E_A_6 |  -.0701812   .0336425    -2.09   0.037    -.1361193   -.0042432
                      E_A_7 |  -.0479498   .0333753    -1.44   0.151    -.1133641    .0174645
                      E_A_8 |     .01418   .0315626     0.45   0.653    -.0476815    .0760415
                      E_A_9 |  -.0249711   .0314092    -0.80   0.427     -.086532    .0365897
                     E_A_10 |  -.0104679   .0305124    -0.34   0.732     -.070271    .0493353
                     E_A_11 |  -.0221077   .0305492    -0.72   0.469     -.081983    .0377676
                     E_A_12 |   .0316241    .030947     1.02   0.307     -.029031    .0922791
                     E_A_13 |  -.0514317   .0333084    -1.54   0.123    -.1167149    .0138515
                     E_A_14 |  -.0539691   .0334332    -1.61   0.106    -.1194969    .0115588
                  NEWS_B_14 |  -.0700026   .0847728    -0.83   0.409    -.2361543     .096149
                  NEWS_B_13 |   .0717995   .0520154     1.38   0.167    -.0301488    .1737479
                  NEWS_B_12 |   .0069548   .0456402     0.15   0.879    -.0824984     .096408
                  NEWS_B_11 |  -.0229596    .034506    -0.67   0.506      -.09059    .0446709
                  NEWS_B_10 |  -.0022119   .0516491    -0.04   0.966    -.1034423    .0990185
                   NEWS_B_9 |    .021007   .0303768     0.69   0.489    -.0385304    .0805444
                   NEWS_B_8 |   .0208946   .0281823     0.74   0.458    -.0343417    .0761309
                   NEWS_B_7 |   .0068795   .0305804     0.22   0.822    -.0530569     .066816
                   NEWS_B_6 |   .0123342   .0224589     0.55   0.583    -.0316845    .0563528
                   NEWS_B_5 |    .002669   .0229439     0.12   0.907    -.0423003    .0476383
                   NEWS_B_4 |   .0034655   .0177543     0.20   0.845    -.0313324    .0382634
                   NEWS_B_3 |    .036563    .024617     1.49   0.137    -.0116854    .0848114
                   NEWS_B_2 |  -.0205021   .0167502    -1.22   0.221    -.0533319    .0123277
                   NEWS_B_1 |    -.00033   .0107223    -0.03   0.975    -.0213453    .0206853
NEWS_ON_THE_GIVEN_EXECUTION |   .0064786   .0066679     0.97   0.331    -.0065902    .0195474
                   NEWS_A_1 |  -.0013603   .0040055    -0.34   0.734     -.009211    .0064904
                   NEWS_A_2 |    .001679   .0103553     0.16   0.871    -.0186171     .021975
                   NEWS_A_3 |  -.0328659    .019803    -1.66   0.097     -.071679    .0059472
                   NEWS_A_4 |  -.0193648   .0245699    -0.79   0.431    -.0675209    .0287914
                   NEWS_A_5 |   -.002906   .0234947    -0.12   0.902    -.0489549    .0431428
                   NEWS_A_6 |   .0347306   .0492041     0.71   0.480    -.0617077    .1311689
                   NEWS_A_7 |    .041503   .0622546     0.67   0.505    -.0805139    .1635198
                   NEWS_A_8 |   .0602249   .0816058     0.74   0.461    -.0997196    .2201693
                   NEWS_A_9 |  -.0591969   .0655428    -0.90   0.366    -.1876584    .0692647
                  NEWS_A_10 |    .195573   .0679927     2.88   0.004     .0623097    .3288363
                  NEWS_A_11 |   .0798503   .0765655     1.04   0.297    -.0702154     .229916
                  NEWS_A_12 |  -.0947258   .0773221    -1.23   0.221    -.2462744    .0568227
                  NEWS_A_13 |   .1570619    .282383     0.56   0.578    -.3963986    .7105224
                  NEWS_A_14 |   .1672226   .1201473     1.39   0.164    -.0682617     .402707
                    TV_B_14 |          0  (omitted)
                    TV_B_13 |          0  (omitted)
                    TV_B_12 |          0  (omitted)
                    TV_B_11 |          0  (omitted)
                    TV_B_10 |          0  (omitted)
                     TV_B_9 |          0  (omitted)
                     TV_B_8 |          0  (omitted)
                     TV_B_7 |          0  (omitted)
                     TV_B_6 |  -.0428597   .0919613    -0.47   0.641    -.2231006    .1373812
                     TV_B_5 |    .039659    .073639     0.54   0.590    -.1046709    .1839889
                     TV_B_4 |   -.196803   .1561913    -1.26   0.208    -.5029323    .1093264
                     TV_B_3 |  -.0592006   .0528817    -1.12   0.263    -.1628468    .0444455
                     TV_B_2 |   -.013776   .0263029    -0.52   0.600    -.0653288    .0377768
                     TV_B_1 |   .0209775   .0144254     1.45   0.146    -.0072959    .0492508
  TV_ON_THE_GIVEN_EXECUTION |  -.0041524   .0116682    -0.36   0.722    -.0270216    .0187168
                     TV_A_1 |  -.0122172   .0356054    -0.34   0.732    -.0820024     .057568
                     TV_A_2 |   .0683548   .1061737     0.64   0.520    -.1397419    .2764515
                     TV_A_3 |   .0338043   .0512842     0.66   0.510    -.0667108    .1343194
                     TV_A_4 |          0  (omitted)
                     TV_A_5 |          0  (omitted)
                     TV_A_6 |          0  (omitted)
                     TV_A_7 |          0  (omitted)
                     TV_A_8 |          0  (omitted)
                     TV_A_9 |          0  (omitted)
                    TV_A_10 |          0  (omitted)
                    TV_A_11 |          0  (omitted)
                    TV_A_12 |          0  (omitted)
                    TV_A_13 |          0  (omitted)
                    TV_A_14 |          0  (omitted)
       ACTIVE_DEATH_PENALTY |  -.2406594   .0138425   -17.39   0.000    -.2677902   -.2135286
                    DUMMY79 |   .1038623   .0115381     9.00   0.000     .0812479    .1264766
                    DUMMY80 |   .1702971   .0112417    15.15   0.000     .1482638    .1923304
                    DUMMY81 |   .1396545   .0112068    12.46   0.000     .1176896    .1616194
                    DUMMY82 |   .0737902   .0112538     6.56   0.000     .0517331    .0958472
                    DUMMY83 |  -.0267957   .0114189    -2.35   0.019    -.0491763   -.0044151
                    DUMMY84 |  -.0558727   .0114311    -4.89   0.000    -.0782773   -.0334681
                    DUMMY85 |  -.0540605   .0113585    -4.76   0.000    -.0763227   -.0317983
                    DUMMY86 |   .0331145   .0110766     2.99   0.003     .0114047    .0548243
                    DUMMY88 |   .0341671   .0110496     3.09   0.002     .0125102    .0558239
                    DUMMY89 |   .0701657    .011025     6.36   0.000     .0485571    .0917743
                    DUMMY90 |   .1553384   .0109137    14.23   0.000     .1339479    .1767289
                    DUMMY91 |   .2267155   .0108346    20.93   0.000     .2054801    .2479508
                    DUMMY92 |   .1766389   .0111526    15.84   0.000     .1547802    .1984977
                    DUMMY93 |   .2027749   .0111971    18.11   0.000      .180829    .2247209
                    DUMMY94 |   .1710272   .0115287    14.83   0.000     .1484312    .1936231
                    DUMMY95 |   .1051533   .0121062     8.69   0.000     .0814256     .128881
                    DUMMY96 |    .042025   .0125862     3.34   0.001     .0173566    .0666935
                    DUMMY97 |  -.0041431   .0128157    -0.32   0.746    -.0292614    .0209752
                    DUMMY98 |  -.0886046   .0131996    -6.71   0.000    -.1144754   -.0627338
                        jan |   .0087214   .0088583     0.98   0.325    -.0086406    .0260833
                        feb |   .0178981   .0088907     2.01   0.044     .0004726    .0353236
                        mar |   .0000288    .008682     0.00   0.997    -.0169877    .0170453
                        may |   .0121328   .0088088     1.38   0.168    -.0051321    .0293977
                        jun |   .0548554   .0086863     6.32   0.000     .0378306    .0718802
                        jul |   .1052167   .0086562    12.16   0.000     .0882508    .1221826
                        aug |   .1278847   .0084727    15.09   0.000     .1112786    .1444908
                        sep |   .0788539   .0087735     8.99   0.000     .0616582    .0960497
                        oct |   .0545133   .0086271     6.32   0.000     .0376045     .071422
                        nov |   .0288345   .0088859     3.24   0.001     .0114184    .0462505
                        dec |   .0447089   .0087813     5.09   0.000     .0274978    .0619199
                  SUN_DUMMY |  -.0908231   .0059002   -15.39   0.000    -.1023873   -.0792589
                  MON_DUMMY |  -.3204155   .0063853   -50.18   0.000    -.3329305   -.3079005
                  TUE_DUMMY |  -.3500223   .0064265   -54.47   0.000    -.3626179   -.3374266
                  WED_DUMMY |  -.3640632   .0063768   -57.09   0.000    -.3765616   -.3515649
                 THUR_DUMMY |  -.3490769   .0063628   -54.86   0.000    -.3615476   -.3366061
                  FRI_DUMMY |  -.2386717   .0061815   -38.61   0.000    -.2507872   -.2265562
                  SAT_DUMMY |          0  (omitted)
                   dummy_29 |   .0116891   .0127503     0.92   0.359    -.0133012    .0366793
                    newyear |   .6339144   .0255311    24.83   0.000     .5838744    .6839544
             NEW_YEAR_LAG_1 |   .0147432   .0338457     0.44   0.663     -.051593    .0810795
                 goodfriday |   .0791237   .0341571     2.32   0.021     .0121771    .1460704
               Easter_LAG_1 |  -.0409146   .0366747    -1.12   0.265    -.1127956    .0309665
               memorial_Day |   .0409262   .0350418     1.17   0.243    -.0277545    .1096069
             MEMORIAL_LAG_1 |   .0633764   .0350396     1.81   0.070       -.0053    .1320528
           independence_Day |   .2133351   .0292998     7.28   0.000     .1559086    .2707616
         INDEPENDENCE_LAG_1 |   .0665028    .031242     2.13   0.033     .0052695     .127736
                  labor_day |     .16592   .0320175     5.18   0.000     .1031669    .2286732
                LABOR_LAG_1 |   .0848864   .0337866     2.51   0.012     .0186659    .1511069
               thanksgiving |   .1769625   .0329809     5.37   0.000     .1123211    .2416039
         THANKSGIVING_LAG_1 |   .0626619   .0329761     1.90   0.057      -.00197    .1272938
                  christmas |   .1591754   .0311707     5.11   0.000     .0980819    .2202689
            CHRISTMAS_LAG_1 |  -.0806795   .0344233    -2.34   0.019     -.148148    -.013211
            STATE_RESIDENTS |   8.75e-08   3.47e-09    25.23   0.000     8.07e-08    9.43e-08
            STATE_PRISONERS |  -7.30e-06   2.57e-07   -28.37   0.000    -7.81e-06   -6.80e-06
        STATE_PRISONER_RATE |   .0003989   .0000329    12.13   0.000     .0003344    .0004633
                    alabama |  -.2465564    .084825    -2.91   0.004    -.4128104   -.0803024
                     alaska |  -2.543436   .1018151   -24.98   0.000    -2.742989   -2.343882
                    arizona |  -.6429442   .0865158    -7.43   0.000    -.8125122   -.4733763
                   arkansas |   -.818303    .089779    -9.11   0.000    -.9942666   -.6423394
                   colorado |  -.9999912   .0882779   -11.33   0.000    -1.173013   -.8269697
                connecticut |  -1.198366   .0888001   -13.50   0.000    -1.372411   -1.024321
                   delaware |  -2.424929   .0995415   -24.36   0.000    -2.620026   -2.229831
              washington_dc |  -1.054117   .0942089   -11.19   0.000    -1.238763   -.8694712
                    florida |   .2036615   .0657035     3.10   0.002      .074885     .332438
                    georgia |   .0377605    .079312     0.48   0.634     -.117688    .1932091
                     hawaii |   -2.38388   .1015984   -23.46   0.000    -2.583009    -2.18475
                      idaho |  -2.580202   .1006868   -25.63   0.000    -2.777545    -2.38286
                   illinois |   .1062626   .0673312     1.58   0.115    -.0257041    .2382292
                    indiana |  -.6521383   .0815463    -8.00   0.000    -.8119661   -.4923105
                       iowa |  -2.301176   .0962359   -23.91   0.000    -2.489794   -2.112557
                     kansas |  -1.548414    .093331   -16.59   0.000    -1.731339   -1.365488
                   kentucky |  -.8386672    .086723    -9.67   0.000    -1.008641   -.6686933
                  louisiana |   .0394487   .0838735     0.47   0.638    -.1249404    .2038378
                      maine |     -2.873   .1048403   -27.40   0.000    -3.078483   -2.667517
                   maryland |  -.3466404   .0834487    -4.15   0.000    -.5101968   -.1830839
              massachusetts |  -1.316125    .084094   -15.65   0.000    -1.480946   -1.151304
                   michigan |  -.1792389   .0757507    -2.37   0.018    -.3277076   -.0307702
                  minnesota |  -1.733601   .0903317   -19.19   0.000    -1.910648   -1.556554
                mississippi |  -.4474252   .0886164    -5.05   0.000    -.6211101   -.2737403
                   missouri |  -.3305303   .0823436    -4.01   0.000    -.4919209   -.1691397
                    montana |  -2.298454    .100593   -22.85   0.000    -2.495612   -2.101295
                   nebraska |  -2.025354   .0969535   -20.89   0.000    -2.215379   -1.835329
                     nevada |  -1.179596   .0933826   -12.63   0.000    -1.362622   -.9965693
              new_hampshire |  -3.074416   .1059573   -29.02   0.000    -3.282089   -2.866744
                 new_jersey |  -.7382442   .0770297    -9.58   0.000    -.8892197   -.5872687
                 new_mexico |  -1.088921   .0930907   -11.70   0.000    -1.271376   -.9064669
                   new_york |    -.08667   .0579171    -1.50   0.135    -.2001854    .0268453
             north_carolina |  -.0937463    .079084    -1.19   0.236     -.248748    .0612554
               north_dakota |  -3.599902   .1136443   -31.68   0.000    -3.822641   -3.377163
                       ohio |  -.5038957   .0700497    -7.19   0.000    -.6411906   -.3666008
                   oklahoma |  -.9589844   .0864763   -11.09   0.000    -1.128475    -.789494
                     oregon |  -1.321537   .0902421   -14.64   0.000    -1.498408   -1.144666
               pennsylvania |  -.4388774   .0664159    -6.61   0.000    -.5690502   -.3087046
               rhode_island |  -2.384441    .101639   -23.46   0.000     -2.58365   -2.185232
             south_carolina |  -.5293928   .0865971    -6.11   0.000    -.6991199   -.3596656
               south_dakota |  -2.749064   .1041148   -26.40   0.000    -2.953125   -2.545003
                  tennessee |  -.1882814   .0829281    -2.27   0.023    -.3508176   -.0257452
                      texas |   .4123111   .0597608     6.90   0.000     .2951822    .5294401
                       utah |  -1.840361   .0962597   -19.12   0.000    -2.029027   -1.651696
                   virginia |  -.3979736   .0802681    -4.96   0.000    -.5552961    -.240651
                    vermont |   -3.43482   .1118958   -30.70   0.000    -3.654131   -3.215508
                 washington |  -1.010182   .0840057   -12.03   0.000     -1.17483   -.8455342
              west_virginia |  -1.637092   .0967528   -16.92   0.000    -1.826724    -1.44746
                  wisconsin |   -1.40902   .0877253   -16.06   0.000    -1.580958   -1.237081
                    wyoming |  -2.999585   .1057375   -28.37   0.000    -3.206827   -2.792343
                      _cons |   1.517704   .0948713    16.00   0.000      1.33176    1.703648
----------------------------+----------------------------------------------------------------
                      /ln_r |   7.527901          .                             .           .
                      /ln_s |   6.732611   .0109951                      6.711061    6.754161
----------------------------+----------------------------------------------------------------
                          r |   1859.199          .                             .           .
                          s |   839.3357   9.228616                      821.4415    857.6198
---------------------------------------------------------------------------------------------
LR test vs. pooled: chibar2(01) = 0.00                 Prob >= chibar2 = 1.000
convergence not achieved
r(430);
 
. estimates store random

    Comment

    • #3
      Moonki:
      the simplest reason why a model fails to converge is that its specification should be improved. The usual recipe is to re-start everything from scracth, adding one predictor at at time and see when the model starts suffering. This procedure is obviously time-consuming (and unavodably so), especially when you deal with monstre model such yours.
      Just out of cusriosity: assuming that the best scenario (convergence) will eventually come alive, how would you explain to your readers/reviewers/supervisor the role of each predictor?
      Kind regards,
      Carlo
      (Stata 19.0)

      Comment

      • #4
        Adding to what Carlo said, I would definitely get read of the variables alabama through Wyoming. You have already -xtset- your data with STATE as the panel variable, so these variables are already accounted for in that way. This may not be the reason your estimations are failing to converge, but including these variables is an incorrect specification in any case. In fact, it is very surprising that Stata did not drop those variables in the -fe- model because they should be collinear with the fixed effects themselves. That this didn't happen makes me wonder if there is also something wrong with your coding of these variables or of the panel variable REF_STATE. In the -re- model, the presence of these indicators makes it extremely difficult for Stata to estimate the REF_STATE level variation.

        But really, Carlo's post is on the right track: start with a minimal model and then add complexity one step at a time.

        Comment

        • #5
          One more thing. Looking at the values of r and s in the output of the -re- estimation, I see that the estimated distribution for the state-level variation in dispersion is a beta distribution that has a very narrow peak. This suggests that, at least after adjustment for all of these variables, there is in fact very little variation in dispersion among the states. The random-effect models all have difficulty converging when the variance at a level is very close to zero; which appears to be the case here. Now, including state-level dummies when state is the panel variable is pretty much asking to have no remaining variation at the state level. So the first, and most important step is to remove those variables from the model. If there is still difficulty getting convergence after you do that, then it suggests that there just isn't a lot of state-level variation in your data, and perhaps a pooled model makes more sense. But that, too, may be the result of adjusting for too many covariates, so that the covariates are already explaining the state-level variation.

          Again, Carlo's advice is superb. Start with a minimal model and add complexity one step at a time. Each time you add a new construct to the model, think carefully about whether it is really appropriate to do so. In particular, covariates that will collectively account for nearly all state-level variation will leave too little variation at the state-level for the analysis to converge. If you really can explain nearly all state-level variation with covariates, then you don't need a two-level model: the pooled analysis is appropriate in that case.

          Comment

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