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  • Poission Regression

    Dear All,

    I will use xtpoisson command for the first time. I have read the Stata document of the command but I have still some questions. I would appreciate if you could clarify the points below:


    1) What is the function of exposure variable? If I use "year" variable as exposure variable, does it mean I control the year effect in the regression model? I put my commands and results with and without YEAR dummies below . Could you please advise me which of the model is more reliable?


    2) According to the stata document for xtpoisson command, xtpoission command can be used with re, fe and pa options. How can I select between the models? I know Hausman test is used to select between random and fixed effects, what about the comparison between pa and re&fe?

    Code:
    . xtpoisson GRI HDI  EF FII  FMI WGI TRADE, re exposure(YEAR)
    
    Fitting Poisson model:
    
    Iteration 0:   log likelihood = -1575.8525  
    Iteration 1:   log likelihood = -1572.6364  
    Iteration 2:   log likelihood = -1572.6314  
    Iteration 3:   log likelihood = -1572.6314  
    
    Fitting full model:
    
    Iteration 0:   log likelihood = -891.78467  
    Iteration 1:   log likelihood = -813.44925  
    Iteration 2:   log likelihood = -784.53822  
    Iteration 3:   log likelihood = -780.32359  
    Iteration 4:   log likelihood = -780.17044  
    Iteration 5:   log likelihood =    -780.17  
    Iteration 6:   log likelihood =    -780.17  
    
    Random-effects Poisson regression               Number of obs     =        168
    Group variable: ID                              Number of groups  =         24
    
    Random effects u_i ~ Gamma                      Obs per group:
                                                                  min =          7
                                                                  avg =        7.0
                                                                  max =          7
    
                                                    Wald chi2(6)      =     298.45
    Log likelihood  =    -780.17                    Prob > chi2       =     0.0000
    
    ------------------------------------------------------------------------------
             GRI |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
             HDI |   10.67069   1.254408     8.51   0.000     8.212095    13.12928
              EF |    .110718   .0529098     2.09   0.036     .0070167    .2144192
             FII |   3.321821   .4692672     7.08   0.000     2.402074    4.241568
             FMI |  -1.182541   .3798706    -3.11   0.002    -1.927073   -.4380081
             WGI |  -.0070223   .0054438    -1.29   0.197    -.0176919    .0036474
           TRADE |   .0046995   .0023287     2.02   0.044     .0001353    .0092636
           _cons |  -12.85906    .975703   -13.18   0.000    -14.77141   -10.94672
        ln(YEAR) |          1  (exposure)
    -------------+----------------------------------------------------------------
        /lnalpha |   .5768572   .2882408                      .0119156    1.141799
    -------------+----------------------------------------------------------------
           alpha |   1.780434   .5131938                      1.011987    3.132398
    ------------------------------------------------------------------------------
    LR test of alpha=0: chibar2(01) = 1584.92              Prob >= chibar2 = 0.000
    
    . xtpoisson GRI HDI  EF FII  FMI WGI TRADE i.YEAR, re exposure(YEAR)
    
    Fitting Poisson model:
    
    Iteration 0:   log likelihood = -1451.9488  
    Iteration 1:   log likelihood =  -1449.335  
    Iteration 2:   log likelihood = -1449.3319  
    Iteration 3:   log likelihood = -1449.3319  
    
    Fitting full model:
    
    Iteration 0:   log likelihood = -775.52875  
    Iteration 1:   log likelihood = -735.45721  
    Iteration 2:   log likelihood = -727.31961  
    Iteration 3:   log likelihood = -726.81391  
    Iteration 4:   log likelihood = -726.80864  
    Iteration 5:   log likelihood = -726.80864  
    
    Random-effects Poisson regression               Number of obs     =        168
    Group variable: ID                              Number of groups  =         24
    
    Random effects u_i ~ Gamma                      Obs per group:
                                                                  min =          7
                                                                  avg =        7.0
                                                                  max =          7
    
                                                    Wald chi2(12)     =     383.64
    Log likelihood  = -726.80864                    Prob > chi2       =     0.0000
    
    ------------------------------------------------------------------------------
             GRI |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
             HDI |  -2.129105   1.810007    -1.18   0.239    -5.676654    1.418444
              EF |   .0816759   .0522113     1.56   0.118    -.0206564    .1840083
             FII |   2.705611   .4744586     5.70   0.000     1.775689    3.635533
             FMI |  -.4729769   .3773934    -1.25   0.210    -1.212654    .2667007
             WGI |   -.000713   .0054791    -0.13   0.896    -.0114519     .010026
           TRADE |   .0001113   .0025765     0.04   0.966    -.0049386    .0051612
                 |
            YEAR |
           2011  |   .2857912   .0524807     5.45   0.000     .1829309    .3886516
           2012  |   .4338611   .0538862     8.05   0.000     .3282461    .5394761
           2013  |   .5952082   .0597691     9.96   0.000      .478063    .7123534
           2014  |   .5961781   .0653963     9.12   0.000     .4680036    .7243525
           2015  |   .6499102   .0737901     8.81   0.000     .5052842    .7945362
           2016  |   .6037155   .0798463     7.56   0.000     .4472196    .7602114
                 |
           _cons |  -4.045653   1.281759    -3.16   0.002    -6.557855   -1.533451
        ln(YEAR) |          1  (exposure)
    -------------+----------------------------------------------------------------
        /lnalpha |  -.3780745   .3327322                     -1.030218    .2740687
    -------------+----------------------------------------------------------------
           alpha |   .6851795   .2279813                      .3569293    1.315305
    ------------------------------------------------------------------------------
    LR test of alpha=0: chibar2(01) = 1445.05              Prob >= chibar2 = 0.000
    Code:
    . xtpoisson GRI HDI  EF FII  FMI WGI TRADE, fe exposure(YEAR)
    
    Iteration 0:   log likelihood = -775.59867  
    Iteration 1:   log likelihood = -599.96341  
    Iteration 2:   log likelihood = -599.43074  
    Iteration 3:   log likelihood = -599.43073  
    
    Conditional fixed-effects Poisson regression    Number of obs     =        168
    Group variable: ID                              Number of groups  =         24
    
                                                    Obs per group:
                                                                  min =          7
                                                                  avg =        7.0
                                                                  max =          7
    
                                                    Wald chi2(6)      =     337.40
    Log likelihood  = -599.43073                    Prob > chi2       =     0.0000
    
    ------------------------------------------------------------------------------
             GRI |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
             HDI |   12.79057   1.217706    10.50   0.000     10.40391    15.17723
              EF |   .1912765   .0506003     3.78   0.000     .0921017    .2904514
             FII |    3.06273   .4725617     6.48   0.000     2.136526    3.988933
             FMI |  -1.283185   .3853587    -3.33   0.001    -2.038474   -.5278962
             WGI |  -.0009267   .0053867    -0.17   0.863    -.0114845    .0096311
           TRADE |   .0078161   .0022502     3.47   0.001     .0034058    .0122265
        ln(YEAR) |          1  (exposure)
    ------------------------------------------------------------------------------
    
    . xtpoisson GRI HDI  EF FII  FMI WGI TRADE i.YEAR, fe exposure(YEAR)
    
    Iteration 0:   log likelihood = -775.59867  
    Iteration 1:   log likelihood =  -562.0786  
    Iteration 2:   log likelihood = -559.96972  
    Iteration 3:   log likelihood = -559.96948  
    Iteration 4:   log likelihood = -559.96948  
    
    Conditional fixed-effects Poisson regression    Number of obs     =        168
    Group variable: ID                              Number of groups  =         24
    
                                                    Obs per group:
                                                                  min =          7
                                                                  avg =        7.0
                                                                  max =          7
    
                                                    Wald chi2(12)     =     391.25
    Log likelihood  = -559.96948                    Prob > chi2       =     0.0000
    
    ------------------------------------------------------------------------------
             GRI |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
             HDI |  -.4062424   2.476247    -0.16   0.870    -5.259596    4.447112
              EF |   .1910001   .0522478     3.66   0.000     .0885964    .2934038
             FII |   2.509861   .4907356     5.11   0.000     1.548037    3.471685
             FMI |  -.7382897   .3941709    -1.87   0.061     -1.51085    .0342711
             WGI |   .0055388   .0057093     0.97   0.332    -.0056512    .0167288
           TRADE |   .0053887   .0025473     2.12   0.034     .0003961    .0103812
                 |
            YEAR |
           2011  |   .2477582   .0535355     4.63   0.000     .1428306    .3526857
           2012  |   .3977779   .0568557     7.00   0.000     .2863428     .509213
           2013  |   .5578187   .0678905     8.22   0.000     .4247558    .6908817
           2014  |   .5495625   .0770526     7.13   0.000     .3985421    .7005829
           2015  |   .6285136   .0872209     7.21   0.000     .4575638    .7994635
           2016  |   .5883933   .0954913     6.16   0.000     .4012337    .7755528
        ln(YEAR) |          1  (exposure)
    ------------------------------------------------------------------------------
    Code:
    . xtpoisson GRI HDI  EF FII  FMI WGI TRADE, pa exposure(YEAR)
    
    Iteration 1: tolerance = 2.3385434
    Iteration 2: tolerance = .20865524
    Iteration 3: tolerance = .0478495
    Iteration 4: tolerance = .01383021
    Iteration 5: tolerance = .0044911
    Iteration 6: tolerance = .00150734
    Iteration 7: tolerance = .00051301
    Iteration 8: tolerance = .00017535
    Iteration 9: tolerance = .00006004
    Iteration 10: tolerance = .00002057
    Iteration 11: tolerance = 7.049e-06
    Iteration 12: tolerance = 2.415e-06
    Iteration 13: tolerance = 8.278e-07
    
    GEE population-averaged model                   Number of obs     =        168
    Group variable:                         ID      Number of groups  =         24
    Link:                                  log      Obs per group:
    Family:                            Poisson                    min =          7
    Correlation:                  exchangeable                    avg =        7.0
                                                                  max =          7
                                                    Wald chi2(6)      =    1246.81
    Scale parameter:                         1      Prob > chi2       =     0.0000
    
    ------------------------------------------------------------------------------
             GRI |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
             HDI |   3.342809   .3234682    10.33   0.000     2.708822    3.976795
              EF |  -.0975146   .0121892    -8.00   0.000    -.1214049   -.0736242
             FII |   3.550038   .1624921    21.85   0.000      3.23156    3.868517
             FMI |  -.7803941   .1158117    -6.74   0.000    -1.007381   -.5534074
             WGI |  -.0188183   .0015491   -12.15   0.000    -.0218546   -.0157821
           TRADE |  -.0091936   .0006503   -14.14   0.000    -.0104681   -.0079192
           _cons |  -5.883861   .2141177   -27.48   0.000    -6.303523   -5.464198
        ln(YEAR) |          1  (exposure)
    ------------------------------------------------------------------------------
    
    . xtpoisson GRI HDI  EF FII  FMI WGI TRADE i.YEAR, pa exposure(YEAR)
    
    Iteration 1: tolerance = .48663477
    Iteration 2: tolerance = .16379105
    Iteration 3: tolerance = .08467556
    Iteration 4: tolerance = .03321535
    Iteration 5: tolerance = .01353904
    Iteration 6: tolerance = .00538494
    Iteration 7: tolerance = .00211855
    Iteration 8: tolerance = .00082768
    Iteration 9: tolerance = .0003224
    Iteration 10: tolerance = .00012541
    Iteration 11: tolerance = .00004875
    Iteration 12: tolerance = .00001895
    Iteration 13: tolerance = 7.364e-06
    Iteration 14: tolerance = 2.862e-06
    Iteration 15: tolerance = 1.112e-06
    Iteration 16: tolerance = 4.321e-07
    
    GEE population-averaged model                   Number of obs     =        168
    Group variable:                         ID      Number of groups  =         24
    Link:                                  log      Obs per group:
    Family:                            Poisson                    min =          7
    Correlation:                  exchangeable                    avg =        7.0
                                                                  max =          7
                                                    Wald chi2(12)     =    1804.22
    Scale parameter:                         1      Prob > chi2       =     0.0000
    
    ------------------------------------------------------------------------------
             GRI |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
             HDI |   -1.32668   .3468089    -3.83   0.000    -2.006413   -.6469471
              EF |   .0179691   .0100962     1.78   0.075    -.0018191    .0377573
             FII |   2.249052   .1565114    14.37   0.000     1.942295    2.555809
             FMI |  -.3056151   .1072055    -2.85   0.004     -.515734   -.0954962
             WGI |  -.0083042   .0015664    -5.30   0.000    -.0113742   -.0052342
           TRADE |  -.0101737   .0006594   -15.43   0.000     -.011466   -.0088814
                 |
            YEAR |
           2011  |   .3365393   .0224792    14.97   0.000     .2924808    .3805977
           2012  |   .4809707   .0230003    20.91   0.000     .4358909    .5260506
           2013  |   .6241108   .0238508    26.17   0.000     .5773642    .6708574
           2014  |   .6194839   .0242414    25.55   0.000     .5719716    .6669962
           2015  |    .642498   .0253509    25.34   0.000     .5928112    .6921849
           2016  |   .5655204   .0259043    21.83   0.000     .5147488    .6162919
                 |
           _cons |  -3.225831   .2112304   -15.27   0.000    -3.639836   -2.811827
        ln(YEAR) |          1  (exposure)
    ------------------------------------------------------------------------------

  • #2
    1) Without the exposure(year) option your model will be as follows. You are modelling the counts.

    ln(GRI) = x'b

    with the exposure(year) option your model will be as follows.

    ln(GRI/YEAR) = x'b

    the typical application of this is when YEAR represents the person-time at risk (or some measure of exposure) so you are modelling the rate (rather than the count). If the variable YEAR contains, for example, number of people or expected counts then this would be standard; if YEAR is calendar year then such a model would be unusual.

    2) The output is telling you which models are being fitted. The choice depend on your study design and what you are trying to estimate.

    Comment


    • #3
      Thank you very much for your answer. I have just an additional question to check whether I understand correctly. I have a variable of "NUMBER" which represents "the number of listed companies" and my dependent variale "GRI" represents "the number of GRI reports". So, if I use exposure(NUMBER), then I am modelling "count of GRI reports per listed companies", am I right?

      Comment


      • #4
        Yes, that’s correct.

        Comment


        • #5
          Thank you very much.

          Comment

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