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  • margins not estimable

    Hi All,

    I run a panel data fixed effect regression. The unit is each agency in each month. Some areas have one agency, but some areas have multiple agencies. From the results, I got the coefficients for the dummy and the interaction term. It's fine that tip length is omitted since it's yearly level characteristic. However, when I try to get the marginal effect using the "margins" command, it returns "not estimable".

    What should I do then? What's the problem here?

    Thanks!


    Best,
    Katie&Linda

    Code:
    . xtset Agency_id Year_Month
           panel variable:  Agency_id (strongly balanced)
            time variable:  Year_Month, 1 to 202
                    delta:  1 unit
    
    . xtreg lnUPT i.Uber_Dummy##c.ln_trip_length_yearly i.Year i.Month i.Area_id, fe r
    note: ln_trip_length_yearly omitted because of collinearity
    note: 2.Area_id omitted because of collinearity
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    note: 335.Area_id omitted because of collinearity
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    note: 342.Area_id omitted because of collinearity
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    note: 353.Area_id omitted because of collinearity
    note: 359.Area_id omitted because of collinearity
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    note: 380.Area_id omitted because of collinearity
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    note: 384.Area_id omitted because of collinearity
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    note: 390.Area_id omitted because of collinearity
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    note: 462.Area_id omitted because of collinearity
    note: 473.Area_id omitted because of collinearity
    note: 487.Area_id omitted because of collinearity
    
    Fixed-effects (within) regression               Number of obs     =    116,554
    Group variable: Agency_id                       Number of groups  =        577
    
    R-sq:                                           Obs per group:
         within  = 0.1211                                         min =        202
         between = 0.0001                                         avg =      202.0
         overall = 0.0480                                         max =        202
    
                                                    F(29,576)         =      30.16
    corr(u_i, Xb)  = 0.0000                         Prob > F          =     0.0000
    
                                                      (Std. Err. adjusted for 577 clusters in Agency_id)
    ----------------------------------------------------------------------------------------------------
                                       |               Robust
                                 lnUPT |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
    -----------------------------------+----------------------------------------------------------------
                          1.Uber_Dummy |  -.3005539   .5226027    -0.58   0.565    -1.326993    .7258853
                 ln_trip_length_yearly |          0  (omitted)
                                       |
    Uber_Dummy#c.ln_trip_length_yearly |
                                    1  |   .0230477     .22767     0.10   0.919    -.4241168    .4702123
                                       |
                                  Year |
                                 2003  |   .4708216   .1319299     3.57   0.000     .2116993    .7299439
                                 2004  |    .720751    .166671     4.32   0.000     .3933939    1.048108
                                 2005  |   1.076519   .1879828     5.73   0.000     .7073037    1.445735
                                 2006  |   1.938873   .2076607     9.34   0.000     1.531009    2.346738
                                 2007  |   2.481783   .2111151    11.76   0.000     2.067133    2.896432
                                 2008  |   2.602228   .2178941    11.94   0.000     2.174264    3.030192
                                 2009  |   2.884311   .2250661    12.82   0.000      2.44226    3.326361
                                 2010  |    3.00545    .230801    13.02   0.000     2.552136    3.458764
                                 2011  |   3.158327   .2329556    13.56   0.000     2.700781    3.615873
                                 2012  |   3.276523   .2444711    13.40   0.000      2.79636    3.756687
                                 2013  |   3.427571   .2507565    13.67   0.000     2.935063     3.92008
                                 2014  |   3.562416   .2680084    13.29   0.000     3.036023    4.088809
                                 2015  |   3.800382   .2914877    13.04   0.000     3.227874    4.372891
                                 2016  |   3.877549   .2986688    12.98   0.000     3.290936    4.464162
                                 2017  |   3.816025   .3085689    12.37   0.000     3.209967    4.422082
                                 2018  |   3.473521   .3120829    11.13   0.000     2.860561     4.08648
                                       |
                                 Month |
                                    2  |  -.0043397   .0048579    -0.89   0.372    -.0138811    .0052016
                                    3  |   .0641212   .0061542    10.42   0.000     .0520337    .0762086
                                    4  |   .0347302   .0091859     3.78   0.000     .0166883    .0527721
                                    5  |   .0007595    .013127     0.06   0.954    -.0250232    .0265422
                                    6  |  -.0567995   .0191928    -2.96   0.003    -.0944959   -.0191031
                                    7  |  -.0876537   .0226005    -3.88   0.000    -.1320432   -.0432642
                                    8  |  -.0048561   .0197063    -0.25   0.805    -.0435611    .0338488
                                    9  |   .0063827   .0194414     0.33   0.743     -.031802    .0445674
                                   10  |  -.0732883   .0222099    -3.30   0.001    -.1169106   -.0296659
                                   11  |   -.092751   .0217057    -4.27   0.000    -.1353829   -.0501191
                                   12  |  -.1584272   .0236513    -6.70   0.000    -.2048806   -.1119739
                                       |
                               Area_id |
                                    2  |          0  (omitted)
                                    3  |          0  (omitted)
                                    4  |          0  (omitted)
                                    5  |          0  (omitted)
                                    7  |          0  (omitted)
                                    8  |          0  (omitted)
                                    9  |          0  (omitted)
                                   10  |          0  (omitted)
                                   11  |          0  (omitted)
                                   12  |          0  (omitted)
                                   13  |          0  (omitted)
                                   14  |          0  (omitted)
                                   15  |          0  (omitted)
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                                   21  |          0  (omitted)
                                   22  |          0  (omitted)
                                   23  |          0  (omitted)
                                   24  |          0  (omitted)
                                   25  |          0  (omitted)
                                   26  |          0  (omitted)
                                   27  |          0  (omitted)
                                   28  |          0  (omitted)
                                   29  |          0  (omitted)
                                   30  |          0  (omitted)
                                   31  |          0  (omitted)
                                   32  |          0  (omitted)
                                   33  |          0  (omitted)
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                                   35  |          0  (omitted)
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                                       |
                                 _cons |   6.866597   .1807507    37.99   0.000     6.511587    7.221608
    -----------------------------------+----------------------------------------------------------------
                               sigma_u |  4.1711054
                               sigma_e |  3.1729999
                                   rho |  .63344096   (fraction of variance due to u_i)
    ----------------------------------------------------------------------------------------------------
    
    . sum ln_trip_length_yearly
    
        Variable |        Obs        Mean    Std. Dev.       Min        Max
    -------------+---------------------------------------------------------
    ln_trip_le~y |    116,554    2.077273     .820816  -1.467504   4.803636
    
    . margins, dydx(Uber_Dummy) at(ln_trip_length_yearly =(-1.5(0.5)5))
    
    Average marginal effects                        Number of obs     =    116,554
    Model VCE    : Robust
    
    Expression   : Linear prediction, predict()
    dy/dx w.r.t. : 1.Uber_Dummy
    
    1._at        : ln_trip_le~y    =        -1.5
    
    2._at        : ln_trip_le~y    =          -1
    
    3._at        : ln_trip_le~y    =         -.5
    
    4._at        : ln_trip_le~y    =           0
    
    5._at        : ln_trip_le~y    =          .5
    
    6._at        : ln_trip_le~y    =           1
    
    7._at        : ln_trip_le~y    =         1.5
    
    8._at        : ln_trip_le~y    =           2
    
    9._at        : ln_trip_le~y    =         2.5
    
    10._at       : ln_trip_le~y    =           3
    
    11._at       : ln_trip_le~y    =         3.5
    
    12._at       : ln_trip_le~y    =           4
    
    13._at       : ln_trip_le~y    =         4.5
    
    14._at       : ln_trip_le~y    =           5
    
    -------------------------------------------------------------------------------
                  |            Delta-method
                  |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
    --------------+----------------------------------------------------------------
    1.Uber_Dummy  |
              _at |
               1  |          .  (not estimable)
               2  |          .  (not estimable)
               3  |          .  (not estimable)
               4  |          .  (not estimable)
               5  |          .  (not estimable)
               6  |          .  (not estimable)
               7  |          .  (not estimable)
               8  |          .  (not estimable)
               9  |          .  (not estimable)
              10  |          .  (not estimable)
              11  |          .  (not estimable)
              12  |          .  (not estimable)
              13  |          .  (not estimable)
              14  |          .  (not estimable)
    -------------------------------------------------------------------------------
    Note: dy/dx for factor levels is the discrete change from the base level.
    
    .
    Last edited by Katie Zhang; 27 Dec 2018, 10:13.

  • #2
    By hiding your information in an attachment, you reduce your chances of getting a timely and helpful response. Many people are understandably unwilling to download an attachment coming from a stranger. The useful way to show Stata output is to copy/paste it from your Results window or log file right here into the Forum editor window, surrounded by code delimiters. If you are not familiar with code delimiters, please read the Forum FAQ with attention to #12.

    Added: It is also the norm in this community to use our real given and surnames as our username to promote collegiality and professionalism. Are you Katie or are you Linda? And what is your real last name? The Forum software does not enable users to edit their username in the profile, but you can click on Contact Us in the lower right corner of this page and send a message to the Forum administrator requesting a change to your correct name (if Katie Zhang is not your correct name).
    Last edited by Clyde Schechter; 27 Dec 2018, 09:55.

    Comment


    • #3
      Sorry, we two share the account. I will copy paste the results.

      Comment


      • #4
        Thanks!

        Comment


        • #5
          You are having this problem because ln_trip_length_yearly is colinear with the year variable you have included in your model. Consequently, some margins you might want to estimate will be inestimable because the results would depend on which variable is omitted to resolve the colinearity. However, the particular marginal effects you want are not subject to that difficulty. So you can get them the old-fashioned way:

          Code:
          forvalues ltly = -1.5(0.5)5 {
              lincom 1.Uber_Dummy + `ltly'*1.Uber_Dummy#c.ln_trip_length_yearly
          }
          Added: You can also get these results by adding the -noestimcheck- option to your -margins- command. The use of that option, however, should always be done with caution because most of the time when Stata says something is not estimable, it really isn't. This is one of the exceptions. It isn't always easy to know what is and isn't estimable, but if the thing you want can be unambiguously calculated using -lincom-, then it's estimable. If you've already done the work of evaluating it with -lincom- and verifying (analytically or through trial and error) that the results do not change when the model is re-parameterized, then using -margins, noestimcheck- to regenerate the same results one more time seems like it's not worth the, admittedly minor, extra effort.
          Last edited by Clyde Schechter; 27 Dec 2018, 12:13.

          Comment


          • #6
            By the way, just tangentially, looking at the coefficients you are getting for year in your output, it looks like there is a more-or-less linear trend over the years here. (Modeling b as a linear function of year, the linear model explains 84% of the variance in b.) You might want to consider replacing i.year with c.year in your model to simplify it. And I would definitely eliminate the Area_ID variable because all of its values just get omitted anyway--they aren't contributing any new information, they're just cluttering up the output.

            Finally, I want to point out that by separately including year and month in your model you are stipulating that there are yearly shocks to the outcome (although as pointed out in the preceding paragraph, it is close to a linear trend over the years) and, superimposed on that, you are modeling "seasonal" (monthly) variation within the year. That's perfectly legitimate--I just raise it so you can verify that that is what you intend to do. I have seen people use this approach when what they really want is to model monthly shocks to the outcome--and this model does not do that.

            Comment


            • #7
              Hi Clyde,

              Apologies for bumping an old thread, but like many I'm struggling with this 'not estimable' problem. I can't seem to find any real problems with the underlying data, and even simple models fail, so I'm at a bit of a loss.

              May I ask why you believe that the marginal effect the OP wanted was not subject to that difficult and can therefore use -noestimcheck-? Since their interaction variable drops it's not clear to me why this is not problematic.

              Best,
              Dan

              Originally posted by Clyde Schechter View Post
              You are having this problem because ln_trip_length_yearly is colinear with the year variable you have included in your model. Consequently, some margins you might want to estimate will be inestimable because the results would depend on which variable is omitted to resolve the colinearity. However, the particular marginal effects you want are not subject to that difficulty. So you can get them the old-fashioned way:

              Added: You can also get these results by adding the -noestimcheck- option to your -margins- command. The use of that option, however, should always be done with caution because most of the time when Stata says something is not estimable, it really isn't. This is one of the exceptions. It isn't always easy to know what is and isn't estimable, but if the thing you want can be unambiguously calculated using -lincom-, then it's estimable. If you've already done the work of evaluating it with -lincom- and verifying (analytically or through trial and error) that the results do not change when the model is re-parameterized, then using -margins, noestimcheck- to regenerate the same results one more time seems like it's not worth the, admittedly minor, extra effort.

              Comment


              • #8
                The interaction variable did not drop. It's clearly there in the output, with a coefficient of .0230477.
                The "main effect" of ln_trip_length_yearly does drop, but that doesn't matter.

                Comment


                • #9
                  Thanks, Clyde. I meant the "main effect".

                  To clarify, the advice you provided to the OP (to either do it manually or give it a noestimcheck option) was since the interaction effect did not drop? Although I don't expect a hard-and-fast answer, this would help me work out whether I should do so as well (along with all the advice in the other posts on this issue).

                  Comment


                  • #10
                    If the interaction effect drops, then the interaction is colinear with something else, and it is not estimable. (You could force an estimate of it by removing whatever it is colinear with from the model, but it is not clear that such an estimate is meaningful.) Usually when an interaction term drops from colinearity it is an indication that the data are incorrect. So if that is what is happening, review your data to make sure that in the estimation sample for your regression there are observations representing all of the possible combinations of the interacted variables. If there are not, then your data are probably not suitable for the analysis you are attempting.

                    Comment

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