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  • Need help: xtabond with Difference GMM and System GMM in Panel Data

    Hello everyone,

    I'm trying to use the Stata 13 to estimate a Dynamic Panel Data with the Difference GMM and System GMM. The first difference equations are:
    Click image for larger version

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    Where:
    Li,t is the dependent variable (here is Leverage)
    Xi,t-1 is the matrix of determinant of the dependent variable (including: prft, tang, growth, size)

    Now, in the Differencing GMM (1991), I want to use the instruments including:
    Click image for larger version

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    and in the System GMM (1998), the instruments I want to use in the first difference equations are
    Click image for larger version

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    and for the level equations are:
    Click image for larger version

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    My attempt is below:

    1. The Difference GMM (1991) estimation:
    Code:
    . xtabond leverage lagleverage lagprft lagsize laggrowth lagtang, lags(2) twostep vce(robust) artests(2) small
    small is a deprecated option
    note: lagleverage dropped because of collinearity
    
    Arellano-Bond dynamic panel-data estimation  Number of obs         =        33
    Group variable: id                           Number of groups      =        11
    Time variable: t
                                                 Obs per group:    min =         3
                                                                   avg =         3
                                                                   max =         3
    
    Number of instruments =     15               Wald chi2(6)          =    109.04
                                                 Prob > chi2           =    0.0000
    Two-step results
                                         (Std. Err. adjusted for clustering on id)
    ------------------------------------------------------------------------------
                 |              WC-Robust
        leverage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
        leverage |
             L1. |  -.0022249   .1895428    -0.01   0.991     -.373722    .3692722
             L2. |   .1227548   .4008548     0.31   0.759    -.6629062    .9084158
                 |
         lagprft |   .1286908   .0810308     1.59   0.112    -.0301267    .2875083
         lagsize |   .1109781   .0509849     2.18   0.030     .0110495    .2109066
       laggrowth |   .0180581    .011342     1.59   0.111    -.0041719     .040288
         lagtang |  -.5714003   .3501781    -1.63   0.103    -1.257737    .1149362
           _cons |  -1.062468   .9021153    -1.18   0.239    -2.830581    .7056455
    ------------------------------------------------------------------------------
    Instruments for differenced equation
            GMM-type: L(2/.).leverage
            Standard: D.lagleverage D.lagprft D.lagsize D.laggrowth D.lagtang
    Instruments for level equation
            Standard: _cons
    2. The System GMM (1998) estimation:
    Code:
    . xtdpdsys leverage lagprft lagtang lagsize laggrowth, lags(2) twostep vce(robust) artests(2)
    
    System dynamic panel-data estimation         Number of obs         =        44
    Group variable: id                           Number of groups      =        11
    Time variable: t
                                                 Obs per group:    min =         4
                                                                   avg =         4
                                                                   max =         4
    
    Number of instruments =     18               Wald chi2(6)          =   1236.16
                                                 Prob > chi2           =    0.0000
    Two-step results
    ------------------------------------------------------------------------------
                 |              WC-Robust
        leverage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
        leverage |
             L1. |   .1122024   .2660097     0.42   0.673     -.409167    .6335719
             L2. |    .232506   .1716271     1.35   0.176     -.103877     .568889
                 |
         lagprft |   .2078303   .1267164     1.64   0.101    -.0405292    .4561899
         lagtang |  -.4553018   .0780344    -5.83   0.000    -.6082465   -.3023571
         lagsize |   .1162271   .0578604     2.01   0.045     .0028228    .2296314
       laggrowth |   .0109966   .0234193     0.47   0.639    -.0349044    .0568975
           _cons |  -1.304568   .8091934    -1.61   0.107    -2.890558    .2814224
    ------------------------------------------------------------------------------
    Instruments for differenced equation
            GMM-type: L(2/.).leverage
            Standard: D.lagprft D.lagtang D.lagsize D.laggrowth
    Instruments for level equation
            GMM-type: LD.leverage
            Standard: _cons
    Also, I have tried to use the xtabond2 command and I got this:
    Code:
    . xtabond2 leverage lagleverage lagprft lagsize lagtang laggrowth, noleveleq two robust small gmm( leverage lagprft lagsize lagtang laggrowth, lag(2 2))
    Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm.
    Warning: Number of instruments may be large relative to number of observations.
    Warning: Two-step estimated covariance matrix of moments is singular.
      Using a generalized inverse to calculate optimal weighting matrix for two-step estimation.
      Difference-in-Sargan statistics may be negative.
    
    Dynamic panel-data estimation, two-step difference GMM
    ------------------------------------------------------------------------------
    Group variable: id                              Number of obs      =        55
    Time variable : t                               Number of groups   =        11
    Number of instruments = 20                      Obs per group: min =         5
    F(5, 11)      =      1.43                                      avg =      5.00
    Prob > F      =     0.288                                      max =         5
    ------------------------------------------------------------------------------
                 |              Corrected
        leverage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
     lagleverage |  -.0217873   .3974929    -0.05   0.957    -.8966633    .8530887
         lagprft |   .0331341   .2335589     0.14   0.890    -.4809257    .5471938
         lagsize |   .0807723    .066558     1.21   0.250    -.0657209    .2272654
         lagtang |  -.4597916   .5534276    -0.83   0.424    -1.677877    .7582943
       laggrowth |   .0237899   .0384265     0.62   0.548    -.0607863     .108366
    ------------------------------------------------------------------------------
    Instruments for first differences equation
      GMM-type (missing=0, separate instruments for each period unless collapsed)
        L2.(leverage lagprft lagsize lagtang laggrowth)
    ------------------------------------------------------------------------------
    Arellano-Bond test for AR(1) in first differences: z =  -0.91  Pr > z =  0.365
    Arellano-Bond test for AR(2) in first differences: z =   0.45  Pr > z =  0.650
    ------------------------------------------------------------------------------
    Sargan test of overid. restrictions: chi2(15)   =  20.23  Prob > chi2 =  0.163
      (Not robust, but not weakened by many instruments.)
    Hansen test of overid. restrictions: chi2(15)   =   9.11  Prob > chi2 =  0.872
      (Robust, but can be weakened by many instruments.)
    I know that there were several mistakes in all of my commands but I just could not figure out and make it right the way I want as I have stated earlier.

    Here, the p_value are quite large and the number of instruments are larger than the number of groups. (I know my sample is too small but sadly I could not change it). I hope you can help me fix the command to get the significant results.

    Also, I am a new Stata user, so please forgive me if I have made any foolish mistakes and feel free to let me know.

    Thank you in advance! I do really hope to hearing from you soon!
    Last edited by Ann Nguyen; 22 Dec 2015, 13:35.

  • #2
    Hello Ann,

    First a couple of comments. You do not need to generate the lags or differences of variables for your estimation, you may just type l.x or d.x . Additionally, you may combine the operators say a lag of the difference ld.x or include higher orders of the operators, say a second lag l2.x. Also, xtabond by default understands that one of your regressors is the lag of the dependent variable, that is why it was dropped in your first estimation.

    Ok, back to your question. The easiest way to achieve what you want is using the xtdpd command. Actually, this is the command that both xtabond and xtdpdsys are using to get their estimates. xtdpd allows you to think about the first equation you posted (in difference), about the equation in levels, and about instruments for each equation. First, I show how to estimate the classic Arellano-Bond model using xtabond and xtdpd . This will help you understand the logic behind xtdpd .

    Code:
    . webuse nlswork, clear
    (National Longitudinal Survey.  Young Women 14-26 years of age in 1968)
    
    . quietly xtabond ln_wage hours tenure
    
    . estimates store xtabond
    
    . quietly xtdpd ln_wage L.ln_wage hour tenure, div(hour tenure) dgmmiv(ln_wage)
    
    . estimates store xtdpd
    
    . estimates table xt*
    
    ----------------------------------------
        Variable |  xtabond       xtdpd     
    -------------+--------------------------
         ln_wage |
             L1. |  .44625092    .44625092  
                 |
           hours | -.00510036   -.00510036  
          tenure |  .01465557    .01465557  
           _cons |  1.1128255    1.1128255  
    ----------------------------------------
    As I stated, both models are equivalent. However, xtdpd does not use the lag of the dependent variable as a regressor by default. Also, you need to input the instrument list by hand where Arellano-Bond gave you the instruments automatically. In other words, to use xtdpd you need to know precisely what instruments you want. Below I discuss and present the output of the xtdpd command to illustrate the logic behind the instruments.

    Code:
    . xtdpd ln_wage L.ln_wage hour tenure, div(hour tenure) dgmmiv(ln_wage)
    
    Dynamic panel-data estimation                   Number of obs     =     10,714
    Group variable: idcode                          Number of groups  =      3,682
    Time variable: year
                                                    Obs per group:
                                                                  min =          1
                                                                  avg =   2.909832
                                                                  max =          8
    
    Number of instruments =     13                  Wald chi2(3)      =     142.71
                                                    Prob > chi2       =     0.0000
    One-step results
    ------------------------------------------------------------------------------
         ln_wage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
         ln_wage |
             L1. |   .4462509   .1592055     2.80   0.005     .1342139    .7582879
                 |
           hours |  -.0051004   .0008008    -6.37   0.000    -.0066698   -.0035309
          tenure |   .0146556   .0103296     1.42   0.156      -.00559    .0349011
           _cons |   1.112825   .2323112     4.79   0.000     .6575039    1.568147
    ------------------------------------------------------------------------------
    Instruments for differenced equation
            GMM-type: L(2/.).ln_wage
            Standard: D.hours D.tenure
    Instruments for level equation
            Standard: _cons
    At the bottom of the table there are two sets of instruments, instruments for the differenced equation and instruments for the level equation. I have no instruments for the level equation because I have an Arellano-Bond model (For more on this topic http://blog.stata.com/tag/xtabond/ ). If I want to incorporate the second lags of the covariates as instruments for the differenced equation (which is one of the things you want), I would type:

    Code:
    . xtdpd ln_wage L.ln_wage hour tenure, div(hour tenure) dgmmiv(ln_wage) ///
    >         div(l2.hours l2.tenure, nodifference)
    
    Dynamic panel-data estimation                   Number of obs     =      3,351
    Group variable: idcode                          Number of groups  =      1,513
    Time variable: year
                                                    Obs per group:
                                                                  min =          1
                                                                  avg =   2.214805
                                                                  max =          4
    
    Number of instruments =     15                  Wald chi2(3)      =     209.50
                                                    Prob > chi2       =     0.0000
    One-step results
    ------------------------------------------------------------------------------
         ln_wage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
         ln_wage |
             L1. |   .1084218   .0384609     2.82   0.005     .0330398    .1838039
                 |
           hours |  -.0052636   .0006769    -7.78   0.000    -.0065903   -.0039368
          tenure |    .034548   .0041938     8.24   0.000     .0263283    .0427678
           _cons |   1.613179   .0651511    24.76   0.000     1.485485    1.740873
    ------------------------------------------------------------------------------
    Instruments for differenced equation
            GMM-type: L(2/.).ln_wage
            Standard: D.hours D.tenure L2.hours L2.tenure
    Instruments for level equation
            Standard: _cons
    If I want to include these instruments in the level equation as well, I would type:

    Code:
    . xtdpd ln_wage L.ln_wage hour tenure, div(hour tenure) dgmmiv(ln_wage) ///
    >         div(l2.hours l2.tenure, nodifference) liv(l2.tenure l2.hours)
    
    Dynamic panel-data estimation                   Number of obs     =      3,351
    Group variable: idcode                          Number of groups  =      1,513
    Time variable: year
                                                    Obs per group:
                                                                  min =          1
                                                                  avg =   2.214805
                                                                  max =          4
    
    Number of instruments =     17                  Wald chi2(3)      =     500.55
                                                    Prob > chi2       =     0.0000
    One-step results
    ------------------------------------------------------------------------------
         ln_wage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
         ln_wage |
             L1. |   .1360027    .038403     3.54   0.000     .0607341    .2112712
                 |
           hours |  -.0038871   .0006156    -6.31   0.000    -.0050937   -.0026805
          tenure |   .0406165   .0032373    12.55   0.000     .0342716    .0469614
           _cons |   1.501681   .0621885    24.15   0.000     1.379794    1.623568
    ------------------------------------------------------------------------------
    Instruments for differenced equation
            GMM-type: L(2/.).ln_wage
            Standard: D.hours D.tenure L2.hours L2.tenure
    Instruments for level equation
            Standard: L2.tenure L2.hours _cons
    If instead of second lags and lags thereafter as instruments (the Arellano-Bond suggested instruments) you want the difference of the second lags and lag differences thereafter

    Code:
    . xtdpd ln_wage L.ln_wage hour tenure, div(hour tenure) dgmmiv(D.ln_wage) ///
    >         div(l2.hours l2.tenure, nodifference) liv(l2.tenure l2.hours)   
    
    Dynamic panel-data estimation                   Number of obs     =      3,351
    Group variable: idcode                          Number of groups  =      1,513
    Time variable: year
                                                    Obs per group:
                                                                  min =          1
                                                                  avg =   2.214805
                                                                  max =          4
    
    Number of instruments =     13                  Wald chi2(3)      =     451.14
                                                    Prob > chi2       =     0.0000
    One-step results
    ------------------------------------------------------------------------------
         ln_wage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
    -------------+----------------------------------------------------------------
         ln_wage |
             L1. |   .2486382    .048452     5.13   0.000     .1536741    .3436022
                 |
           hours |  -.0040958   .0006515    -6.29   0.000    -.0053727   -.0028189
          tenure |   .0335777   .0038119     8.81   0.000     .0261065     .041049
           _cons |   1.342132   .0753903    17.80   0.000     1.194369    1.489894
    ------------------------------------------------------------------------------
    Instruments for differenced equation
            GMM-type: L(2/.).D.ln_wage
            Standard: D.hours D.tenure L2.hours L2.tenure
    Instruments for level equation
            Standard: L2.tenure L2.hours _cons
    To summarize, I would suggest that you use xtdpd to fit the models you want. I would also suggest you verify at the bottom of the output table if the instruments you used are the ones you want. Finally, I would suggest you look at the documentation to see the options for inputting instruments which I did not describe comprehensively here.

    Comment


    • #3
      Thank you so much for your help, Mr. Enrique Pinzon. I am deeply appreciated it and it does help me a lot.

      I have made an effort to follow your suggestion, but as I type the command, I got an error like this:

      Code:
      . xtdpd leverage l.leverage l.prft l.size l.depr l.growth l.liq div(l.prft l.size l.depr l.growth l.liq) dgmmiv(leverage) div(l3.prft l3.size l3.depr l3.growth l3.liq, nodifference)
      factor variables not allowed
      r(101);
      
      . xtdpd leverage l.leverage l.prft l.size l.depr l.growth l.liq div(l.prft l.size l.depr l.growth l.liq) dgmmiv(d.leverage) div(l3.prft l3.size l3.depr l3.growth l3.liq, nodifference) liv(l3.prft l3.size l3.depr l3.growth l3.liq)
      factor variables not allowed
      r(101);
      Are there something wrong in these command I typed and how to fix it?

      And another question is that if I type l.x to generate the lags of variables of my estimation, so what would be the lags of the first observations?

      I would be truly grateful if you can help me with these problems, Mr. Enrique Pinzon.

      Thank you in advance!
      Last edited by Ann Nguyen; 02 Jan 2016, 15:36.

      Comment


      • #4
        Dear Mr. Enrique Pinzon,

        I would like to update my effort. For the error above, I think I need to generate the lag variables to make these commands work. And here is the result:

        1. The first estimation
        Code:
        . xtdpd leverage l.leverage lprft lsize ldepr lgrowth lliq, dgmmiv(leverage) div(lprft lsize ldepr lgrowth lliq) div(l3.prft l3.size l3.depr l3.growth l3.liq, nodifference) artests(2)
        
        Dynamic panel-data estimation                Number of obs         =        84
        Group variable: id                           Number of groups      =        28
        Time variable: t
                                                     Obs per group:    min =         3
                                                                       avg =         3
                                                                       max =         3
        
        Number of instruments =     20               Wald chi2(6)          =      4.94
                                                     Prob > chi2           =    0.5511
        One-step results
        ------------------------------------------------------------------------------
            leverage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
        -------------+----------------------------------------------------------------
            leverage |
                 L1. |   .0047451   .2199365     0.02   0.983    -.4263225    .4358128
                     |
               lprft |  -.0022327   .0143023    -0.16   0.876    -.0302647    .0257993
               lsize |   .0419332   .0356789     1.18   0.240    -.0279961    .1118625
               ldepr |   .0534486   .1663922     0.32   0.748    -.2726742    .3795714
             lgrowth |  -.0090772   .0154726    -0.59   0.557    -.0394029    .0212485
                lliq |  -.0108712   .0131736    -0.83   0.409    -.0366909    .0149485
               _cons |  -.1101023   .4596063    -0.24   0.811    -1.010914    .7907096
        ------------------------------------------------------------------------------
        Instruments for differenced equation
                GMM-type: L(2/.).leverage
                Standard: D.lprft D.lsize D.ldepr D.lgrowth D.lliq L3.prft L3.size L3.depr L3.growth L3.liq
        Instruments for level equation
                Standard: _cons
        2. The second estimation
        Code:
        . xtdpd leverage l.leverage lprft lsize ldepr lgrowth lliq, dgmmiv(d.leverage) div(lprft lsize ldepr lgrowth lliq) div(l3.prft l3.size l3.depr l3.growth l3.liq, nodifference) liv (l3.prft l3.size l3.depr l3.growth l3.liq) artests(2)
        
        Dynamic panel-data estimation                Number of obs         =        84
        Group variable: id                           Number of groups      =        28
        Time variable: t
                                                     Obs per group:    min =         3
                                                                       avg =         3
                                                                       max =         3
        
        Number of instruments =     22               Wald chi2(6)          =     89.49
                                                     Prob > chi2           =    0.0000
        One-step results
        ------------------------------------------------------------------------------
            leverage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
        -------------+----------------------------------------------------------------
            leverage |
                 L1. |   .7326914   .1608005     4.56   0.000     .4175283    1.047855
                     |
               lprft |   .0077196   .0196748     0.39   0.695    -.0308423    .0462815
               lsize |    .003978   .0083747     0.48   0.635    -.0124361    .0203922
               ldepr |  -.0451645   .0388579    -1.16   0.245    -.1213247    .0309957
             lgrowth |   -.001023   .0121203    -0.08   0.933    -.0247783    .0227323
                lliq |   .0043455   .0156223     0.28   0.781    -.0262737    .0349647
               _cons |   .0699057     .17646     0.40   0.692    -.2759496    .4157609
        ------------------------------------------------------------------------------
        Instruments for differenced equation
                GMM-type: L(2/.).D.leverage
                Standard: D.lprft D.lsize D.ldepr D.lgrowth D.lliq L3.prft L3.size L3.depr L3.growth L3.liq
        Instruments for level equation
                Standard: L3.prft L3.size L3.depr L3.growth L3.liq _cons
        But I am not really sure that these estimations using the instruments that I would like to:
        As stated earlier, for the first estimation, I would like to use the second lags of variables (both dependent and independence variables) as instruments; but the output table shows:
        Code:
        Instruments for differenced equation
                GMM-type: L(2/.).leverage
                Standard: D.lprft D.lsize D.ldepr D.lgrowth D.lliq L3.prft L3.size L3.depr L3.growth L3.liq
        Instruments for level equation
                Standard: _cons
        and likewise, for the second estimation, I would like to use the second lags of variables as instrument for the difference equation and the difference of second lags of variables for the level equation; and the output table shows:
        Code:
        Instruments for differenced equation
                GMM-type: L(2/.).D.leverage
                Standard: D.lprft D.lsize D.ldepr D.lgrowth D.lliq L3.prft L3.size L3.depr L3.growth L3.liq
        Instruments for level equation
                Standard: L3.prft L3.size L3.depr L3.growth L3.liq _cons
        I hope you can help me to verify these result is right what I'm looking for; and also, all of my results seems to be not significant (P_value are quite large), so how to improve it?

        I am deeply appreciated your help and looking forward to hearing from you soon!

        Thank you!
        Last edited by Ann Nguyen; 03 Jan 2016, 03:07.

        Comment


        • #5
          I'm late to this discussion, and these comments apply largely to post #3 from yesterday, not to post #4 immediately above.

          Your two error messages are very strange, since the commands you have copied do not appear to use any factor variables in them. If these were not copied directly from your results window, are you sure none of the variables was typed as 1.x (digit one) instead of l.x (lower case letter L) because 1.x would be a factor variable. As a matter of practice I use L.x with the upper-case L instead of l.x precisely to make this sort of error noticeable.

          And another question is that if I type l.x to generate the lags of variables of my estimation, so what would be the lags of the first observations?
          The lag of a variable L.x in the first observation for a panel will be a missing value, so the first observation of each panel will be excluded from the modeling. Similarly if you use L3.x for the value lagged three periods, it will be missing in each of the first three observations for each panel, and those observations will be excluded from the modeling. That is as it should be. If in creating your lagged variables manually you did something different you should be concerned. In particular, with panel data you want to be careful about using Stata observations like x[_n-1] unless you are using it with the by command.

          Finally, from post #4 it appears you have just three observations for each of your 28 groups. I am not experienced with xtdpd but in most panel data I would say that that is a very small sample.

          Comment


          • #6
            Dear Mr. William Lisowski,

            Thank you for your help. Since the limitation of the sample, I only have 6 periods of observation (T=6). So in the estimation, when I use L3.x for the instrumental variables, then I have 3 observation periods left. Could you tell me how to improve the result with these small sample?

            And another question is that, do you think that I should do the tests for panel cointergration and panel autocorrelation before estimation? Why or why not?

            Thank you so much! I am looking to hear from you!
            Last edited by Ann Nguyen; 03 Jan 2016, 17:51.

            Comment


            • #7
              Hello Ann,

              With regard to your question about second lags. If you want second lags in your first specification where you have L3. change it to L2. With regard to the lagged second difference the same comment applies.

              Comment


              • #8
                Thank you so much for your help, Mr. Enrique Pinzon!

                I have edit the command to follow your suggestion. Here is the result:
                Code:
                . xtdpd leverage l.leverage lprft lsize ldepr lgrowth lliq, dgmmiv(d.leverage) div(lprft lsize ldepr lgrowth lliq) div(L2.lprft L2.lsize L2.ldepr L3.lgrowth L2.lliq,
                >  nodifference) liv (L2.lprft L2.lsize L2.ldepr L2.lgrowth L2.lliq) artests(2)
                
                Dynamic panel-data estimation                Number of obs         =        56
                Group variable: id                           Number of groups      =        28
                Time variable: t
                                                             Obs per group:    min =         2
                                                                               avg =         2
                                                                               max =         2
                
                Number of instruments =     21               Wald chi2(6)          =     95.43
                                                             Prob > chi2           =    0.0000
                One-step results
                ------------------------------------------------------------------------------
                    leverage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
                -------------+----------------------------------------------------------------
                    leverage |
                         L1. |   .7090357   .1249011     5.68   0.000     .4642339    .9538374
                             |
                       lprft |   .3683978   .1052952     3.50   0.000      .162023    .5747726
                       lsize |   .0024165   .0082489     0.29   0.770    -.0137509     .018584
                       ldepr |  -.0968608   .0405421    -2.39   0.017    -.1763218   -.0173998
                     lgrowth |  -.0099702   .0110007    -0.91   0.365    -.0315313    .0115908
                        lliq |  -.0116167   .0149565    -0.78   0.437    -.0409309    .0176975
                       _cons |   .1087505   .1494608     0.73   0.467    -.1841873    .4016883
                ------------------------------------------------------------------------------
                Instruments for differenced equation
                        GMM-type: L(2/.).D.leverage
                        Standard: D.lprft D.lsize D.ldepr D.lgrowth D.lliq L2.lprft L2.lsize L2.ldepr L3.lgrowth L2.lliq
                Instruments for level equation
                        Standard: L2.lprft L2.lsize L2.ldepr L2.lgrowth L2.lliq _cons
                How about the alternative command like this:
                Code:
                xtdpd leverage l.leverage lprft lsize ldepr lgrowth lliq, twostep dgmmiv(leverage, lagrange(2 .)) lgmmiv(leverage, lag(2)) div(l2.lprft l2.lsize l2.ldepr l2.lgro
                > wth l2.lliq, nodifference) liv(L2D.lprft L2D.lsize L2D.ldepr L2D.lgrowth L2D.lliq) artests(2)
                
                Dynamic panel-data estimation                Number of obs         =        56
                Group variable: id                           Number of groups      =        28
                Time variable: t
                                                             Obs per group:    min =         2
                                                                               avg =         2
                                                                               max =         2
                
                Number of instruments =     20               Wald chi2(6)          =    774.76
                                                             Prob > chi2           =    0.0000
                Two-step results
                ------------------------------------------------------------------------------
                    leverage |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
                -------------+----------------------------------------------------------------
                    leverage |
                         L1. |   .8456332   .1151733     7.34   0.000     .6198977    1.071369
                             |
                       lprft |   .3890224   .0682533     5.70   0.000     .2552484    .5227964
                       lsize |   .0389497   .0210479     1.85   0.064    -.0023034    .0802028
                       ldepr |   .1962439   .1511409     1.30   0.194    -.0999868    .4924745
                     lgrowth |   .0002499   .0185466     0.01   0.989    -.0361007    .0366005
                        lliq |  -.0258246   .0105452    -2.45   0.014    -.0464928   -.0051565
                       _cons |  -.5252966     .28502    -1.84   0.065    -1.083926    .0333324
                ------------------------------------------------------------------------------
                Warning: gmm two-step standard errors are biased; robust standard
                         errors are recommended.
                Instruments for differenced equation
                        GMM-type: L(2/.).leverage
                        Standard: L2.lprft L2.lsize L2.ldepr L2.lgrowth L2.lliq
                Instruments for level equation
                        GMM-type: L2D.leverage
                        Standard: L2D.lprft L2D.lsize L2D.ldepr L2D.lgrowth L2D.lliq _cons
                Both results are different and I wonder which one I should use to carry out the estimation that I want (base on the purpose of instrumental variables I stated on post #1)?

                Thank you so much again and I hope to hear from you soon!

                Best,
                Ann Nguyen
                Last edited by Ann Nguyen; 04 Jan 2016, 08:06.

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                • #9
                  For the post #8, I mentioned the results for System-GMM estimation, in which, the the instruments I want to use in the first difference equations are:
                  Click image for larger version

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                  and for the level equations are:
                  Click image for larger version

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                  Thank you! I hope to hear from you soon!
                  Last edited by Ann Nguyen; 04 Jan 2016, 09:41.

                  Comment


                  • #10
                    Hello Ann,

                    The second specification has the instrument set that you want, if I understood correctly.

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