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  • Thank you for your help. Does it look better with one more lags like these?

    Code:
    . xtdpdgmm gdpgrow sme inflation gfcfgrow hfcegrow tradeopen l.lrgdpopc ,gmm(gdpgrow inflation  gfcfgrow hfcegr
    > ow lrgdpopc , lag(2 3) collapse model(diff)) gmm(gdpgrow inflation  gfcfgrow hfcegrow lrgdpopc, lag(1 2) diff
    >  collapse model(level)) iv(sme,model(level))  one vce(cl id) small overid
    
    Generalized method of moments estimation
    
    Fitting full model:
    Step 1         f(b) =  6.5119593
    
    Fitting reduced model 1:
    Step 1         f(b) =  1.7495734
    
    Fitting reduced model 2:
    Step 1         f(b) =  2.0088941
    
    Fitting reduced model 3:
    Step 1         f(b) =  6.4917358
    
    Fitting no-level model:
    Step 1         f(b) =  2.0088941
    
    Group variable: id                           Number of obs         =       919
    Time variable: year                          Number of groups      =        21
    
    Moment conditions:     linear =      22      Obs per group:    min =         6
                        nonlinear =       0                        avg =   43.7619
                            total =      22                        max =        46
    
                                        (Std. err. adjusted for 21 clusters in id)
    ------------------------------------------------------------------------------
                 |               Robust
         gdpgrow | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
    -------------+----------------------------------------------------------------
             sme |   .9128761   .2466531     3.70   0.001     .3983667    1.427386
       inflation |  -.1421567   .0417887    -3.40   0.003    -.2293263   -.0549871
        gfcfgrow |   .1127319   .0368393     3.06   0.006     .0358865    .1895773
        hfcegrow |   .6901123   .1433662     4.81   0.000     .3910556     .989169
       tradeopen |  -.0239375   .0116199    -2.06   0.053    -.0481762    .0003012
                 |
        lrgdpopc |
             L1. |  -.8863398   .8513687    -1.04   0.310    -2.662264    .8895842
                 |
           _cons |   10.84036   8.734277     1.24   0.229    -7.379021    29.05974
    ------------------------------------------------------------------------------
    Instruments corresponding to the linear moment conditions:
     1, model(diff):
       L2.gdpgrow L3.gdpgrow L2.inflation L3.inflation L2.gfcfgrow L3.gfcfgrow
       L2.hfcegrow L3.hfcegrow L2.lrgdpopc L3.lrgdpopc
     2, model(level):
       L1.D.gdpgrow L2.D.gdpgrow L1.D.inflation L2.D.inflation L1.D.gfcfgrow
       L2.D.gfcfgrow L1.D.hfcegrow L2.D.hfcegrow L1.D.lrgdpopc L2.D.lrgdpopc
     3, model(level):
       sme
     4, model(level):
       _cons

    Comment


    • The standard errors of the lagged dependent variable are now indeed much smaller, but this way too large for any meaningful inference. You might just conclude that you cannot reliably estimate this coefficient, and estimate a static model without lagged dependent variable instead.
      https://twitter.com/Kripfganz

      Comment


      • You mean like this?

        Code:
        . xtdpdgmm gdpgrow sme inflation gfcfgrow hfcegrow tradeopen l.lrgdpopc ,gmm( inflation  gfcfgrow hfcegrow lrgd
        > popc , lag(2 3) collapse model(diff)) gmm( inflation  gfcfgrow hfcegrow lrgdpopc, lag(1 2) diff collapse mode
        > l(level)) iv(sme,model(level))  one vce(cl id) small overid
        
        Generalized method of moments estimation
        
        Fitting full model:
        Step 1         f(b) =  4.7191492
        
        Fitting reduced model 1:
        Step 1         f(b) =   .3986962
        
        Fitting reduced model 2:
        Step 1         f(b) =  .93970412
        
        Fitting reduced model 3:
        Step 1         f(b) =  4.5853153
        
        Fitting no-level model:
        Step 1         f(b) =  .93970412
        
        Group variable: id                           Number of obs         =       919
        Time variable: year                          Number of groups      =        21
        
        Moment conditions:     linear =      18      Obs per group:    min =         6
                            nonlinear =       0                        avg =   43.7619
                                total =      18                        max =        46
        
                                            (Std. err. adjusted for 21 clusters in id)
        ------------------------------------------------------------------------------
                     |               Robust
             gdpgrow | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
        -------------+----------------------------------------------------------------
                 sme |   .6794376   .2367041     2.87   0.009     .1856815    1.173194
           inflation |  -.1636133    .045614    -3.59   0.002    -.2587624   -.0684641
            gfcfgrow |   .0980104   .0328853     2.98   0.007     .0294129     .166608
            hfcegrow |   .6692242   .1605369     4.17   0.000     .3343501    1.004098
           tradeopen |  -.0166245   .0134452    -1.24   0.231    -.0446707    .0114218
                     |
            lrgdpopc |
                 L1. |  -1.220907   1.060466    -1.15   0.263       -3.433     .991187
                     |
               _cons |   14.09685   10.72928     1.31   0.204    -8.284046    36.47775
        ------------------------------------------------------------------------------
        Instruments corresponding to the linear moment conditions:
         1, model(diff):
           L2.inflation L3.inflation L2.gfcfgrow L3.gfcfgrow L2.hfcegrow L3.hfcegrow
           L2.lrgdpopc L3.lrgdpopc
         2, model(level):
           L1.D.inflation L2.D.inflation L1.D.gfcfgrow L2.D.gfcfgrow L1.D.hfcegrow
           L2.D.hfcegrow L1.D.lrgdpopc L2.D.lrgdpopc
         3, model(level):
           sme
         4, model(level):
           _cons

        Comment


        • No, I meant removing L.lrgdpopc from the model entirely (both as regressor and instruments).
          https://twitter.com/Kripfganz

          Comment


          • Thank you for giving me advice on this. lrgdpopc is real GDP per capita (level), whereas gdpgrowth is annual GDP growth rate. The former is included to account for conditional convergence. So, I should remove lrgdpopc both as regressor and instrument?

            Comment


            • Initially, you put it in because that is what economic theory tells you. Then you conclude that you cannot obtain a reliable estimate; i.e., you cannot reject the null that there is no conditional convergence, but you can also hardly reject the null of any other meaningful value for this coefficient. Thus, in the final step you estimate the model without it to obtain more efficient estimates for the remaining coefficients.
              https://twitter.com/Kripfganz

              Comment


              • Thank you, Sebastian. I started learning system-GMM two weeks ago and am still in the process of learning it. Your advice has been tremendously helpful, and I am deeply grateful.

                When I remove the lagged level of real GDP per capita, the results look this. Does it look okay?:

                Code:
                . xtdpdgmm gdpgrow sme inflation gfcfgrow hfcegrow tradeopen ,gmm(gdpgrow inflation  gfcfgrow hfcegrow , lag(2 
                > 2) collapse model(diff)) gmm(gdpgrow inflation  gfcfgrow hfcegrow , lag(1 1) diff collapse model(level)) iv(s
                > me,model(level))  one vce(cl id) small overid 
                
                Generalized method of moments estimation
                
                Fitting full model:
                Step 1         f(b) =  2.1473921
                
                Fitting reduced model 1:
                Step 1         f(b) =  1.340e-18
                
                Fitting reduced model 2:
                Step 1         f(b) =  9.209e-16
                
                Fitting reduced model 3:
                Step 1         f(b) =   2.099871
                
                Group variable: id                           Number of obs         =       959
                Time variable: year                          Number of groups      =        21
                
                Moment conditions:     linear =      10      Obs per group:    min =        41
                                    nonlinear =       0                        avg =  45.66667
                                        total =      10                        max =        46
                
                                                    (Std. err. adjusted for 21 clusters in id)
                ------------------------------------------------------------------------------
                             |               Robust
                     gdpgrow | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
                -------------+----------------------------------------------------------------
                         sme |   3.166296   1.565061     2.02   0.057    -.0983644    6.430957
                   inflation |  -.2519669   .0893019    -2.82   0.011    -.4382475   -.0656863
                    gfcfgrow |   .2801333   .0899877     3.11   0.005     .0924223    .4678443
                    hfcegrow |   .3007462   .3989567     0.75   0.460    -.5314629    1.132955
                   tradeopen |  -.0859884   .0266657    -3.22   0.004    -.1416121   -.0303647
                       _cons |   6.186441    2.29732     2.69   0.014     1.394314    10.97857
                ------------------------------------------------------------------------------
                Instruments corresponding to the linear moment conditions:
                 1, model(diff):
                   L2.gdpgrow L2.inflation L2.gfcfgrow L2.hfcegrow
                 2, model(level):
                   L1.D.gdpgrow L1.D.inflation L1.D.gfcfgrow L1.D.hfcegrow
                 3, model(level):
                   sme
                 4, model(level):
                   _cons
                Many thanks for your generous and kind help.

                Taka

                Comment


                • Dear Prof. Sebastian Kripfganz
                  I have two questions about the specification of the variables:
                  1) Our sample includes the year 2020, and we want to control for it. Therefore, we add a dummy variable for this year. Should we specify this variable as an exogenous variable?
                  2) Our dataset includes dummy variables for the availability of technology in some countries in our sample, we want to interact this variable with our main variable of interest. How should we specify the dummy variables and the interaction variables? Should they be exogenous, predetermined, or endogenous variables?

                  Thanks a lot!

                  Comment


                  • 1) Such time dummies can typically be treated as fully exogenous.
                    2) There is no general answer; it depends on whether you can justify the exogeneity of these dummy variables. It certainly seems likely that these dummy variables are correlated with the unobserved country-specific effects. It could be reasonable to assume that they are strictly exogenous with respect to the idiosyncratic error component. With regard to interaction terms, there exogeneity is typically driven by the weakest interaction component; i.e., if the main variable of interest is endogenous, then the interaction terms should normally be treated as endogenous as well.
                    https://twitter.com/Kripfganz

                    Comment


                    • dear Proffesor @Sebastian Kripfganz,


                      I am estimating the effects of Islamic banks and convectıonal banks on underground economy within the OIC natıons

                      I then set up dummıes of Islamic banks to be islamicoic and dummies for non ıslamic banks to be nonislamicoic

                      code :

                      [xtabond2 se L.se ATM CBBRNCH10K DEP1KSWTCHCB BORRWERZ1KCB DOMCREDPRVTSECGDP CAPTOASSETRATIO fxreal Taxes gdppercapitagrowthannualnygdppca, robust nomata iv(L2.ATM L2.CBBRNCH10K L2.DEP1KSWTCHCB L2.BORRWERZ1KCB L2.DOMCREDPRVTSECGDP L2.CAPTOASSETRATIO L2.fxreal L2.Taxes L2.gdppercapitagrowthannualnygdppca ) gmm(L.se l.islamicoıc,collapse)]

                      these are the results ı got are they correct
                      se Coef. St.Err. t-value p-value [95% Conf Interval] Sig
                      L .968 .033 29.11 0 .903 1.033 ***
                      ATM 0 .001 -0.32 .748 -.002 .001
                      CBBRNCH10K 0 .001 0.22 .828 -.001 .001
                      DEP1KSWTCHCB .001 .001 1.00 .316 -.001 .002
                      BORRWERZ1KCB .002 .001 2.63 .008 .001 .004 ***
                      DOMCREDPRVTSECGDP 0 .001 0.84 .402 -.001 .002
                      CAPTOASSETRATIO -.003 .001 -2.27 .023 -.006 0 **
                      fxreal .003 .001 2.12 .034 0 .006 **
                      Taxes -.001 .001 -0.78 .437 -.003 .001
                      gdppercapitagrowth~a -.089 .033 -2.67 .008 -.154 -.024 ***
                      Constant .751 1.271 0.59 .555 -1.74 3.242
                      Mean dependent var 34.572 SD dependent var 9.808
                      Number of obs 735 Chi-square 3697.568
                      *** p<.01, ** p<.05, * p<.1
                      Instruments for first differences equation
                      Standard
                      D.(L2.ATM L2.CBBRNCH10K L2.DEP1KSWTCHCB L2.BORRWERZ1KCB
                      L2.DOMCREDPRVTSECGDP L2.CAPTOASSETRATIO L2.fxreal L2.Taxes
                      L2.gdppercapitagrowthannualnygdppca)
                      GMM-type (missing=0, separate instruments for each period unless collapsed)
                      L(1/.).(L.se L.islamicoıc) collapsed
                      Instruments for levels equation
                      Standard
                      _cons
                      L2.ATM L2.CBBRNCH10K L2.DEP1KSWTCHCB L2.BORRWERZ1KCB L2.DOMCREDPRVTSECGDP
                      L2.CAPTOASSETRATIO L2.fxreal L2.Taxes L2.gdppercapitagrowthannualnygdppca
                      GMM-type (missing=0, separate instruments for each period unless collapsed)
                      D.(L.se L.islamicoıc) collapsed
                      Arellano-Bond test for AR(1) in first differences: z = -3.68 Pr > z = 0.000
                      Arellano-Bond test for AR(2) in first differences: z = -0.90 Pr > z = 0.366
                      Sargan test of overid. restrictions: chi2(30) = 40.85 Prob > chi2 = 0.089
                      (Not robust, but not weakened by many instruments.)
                      Hansen test of overid. restrictions: chi2(30) = 31.85 Prob > chi2 = 0.375
                      (Robust, but weakened by many instruments.)
                      Last edited by Mrisho Rajabu Mrisho; 02 Aug 2023, 10:37.

                      Comment


                      • Thanks Prof. Sebastian Kripfganz for your answer in #594

                        I have one more question. You have introduced a package for Bias Corrected Estimator in Stata. If I am estimating the following model:
                        Y = L.Y + X1 + X2 + ai + u

                        If there is a reverse causality from Y to X2, resulting in an endogeneity problem. Would estimating the model with the Bias Corrected Estimator, while controlling for the individual specific effects, control for the endogeneity of X2 as well? or does it only control for that of L.Y?

                        Comment


                        • No, the bias-corrected estimator implemented in xtdpdbc does not account for reverse causality. It requires all X-regressors to be strictly exogenous in that regard.
                          https://twitter.com/Kripfganz

                          Comment


                          • Dear Prof. Sebastian Kripfganz
                            If the second order autocorrelation is significant, what should I do to control for this rather than adding time fixed effects?

                            Comment


                            • Possible remedies for serial correlation are the inclusion of higher-order autoregressive lags of the dependent variable or distributed lag of the other variables as additional regressors.
                              https://twitter.com/Kripfganz

                              Comment


                              • I have an elementary question about the use of the iv() option in xtdpdgmm. In the command below, "smei4" shows up both as an independent variable and as an instrumental variable in iv(). Is this command problematic? Do I need to lag the instrumental variable like "iv(smei4,lag(1 1) model(level))"? Is it also legitimate to use it like "iv(smei4,lag(0 1) model(level))" or is it problematic again?

                                Thank you for your help.

                                Code:
                                . xtdpdgmm gdpgrow smei4 inflation gfcfgrow hfcegrow tradeopen l.lrgdpopc if id~=10,gmm(gdpgrow inflation  gfcf
                                > grow hfcegrow , lag(2 2) collapse model(diff)) gmm(gdpgrow inflation  gfcfgrow hfcegrow , lag(1 1) diff colla
                                > pse model(level)) iv(smei4,model(level))  one vce(cl id) small
                                
                                Generalized method of moments estimation
                                
                                Fitting full model:
                                Step 1         f(b) =  .47394918
                                
                                Group variable: id                           Number of obs         =       541
                                Time variable: year                          Number of groups      =        20
                                
                                Moment conditions:     linear =      10      Obs per group:    min =         3
                                                    nonlinear =       0                        avg =     27.05
                                                        total =      10                        max =        29
                                
                                                                    (Std. err. adjusted for 20 clusters in id)
                                ------------------------------------------------------------------------------
                                             |               Robust
                                     gdpgrow | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
                                -------------+----------------------------------------------------------------
                                       smei4 |   .2788973   .0764853     3.65   0.002     .1188117    .4389829
                                   inflation |   -.026597   .1618153    -0.16   0.871    -.3652804    .3120864
                                    gfcfgrow |   .2534804   .0579185     4.38   0.000     .1322556    .3747052
                                    hfcegrow |   .2839644   .2120591     1.34   0.196    -.1598803    .7278091
                                   tradeopen |  -.0363412    .008684    -4.18   0.001     -.054517   -.0181655
                                             |
                                    lrgdpopc |
                                         L1. |  -.6204065   1.328873    -0.47   0.646     -3.40177    2.160957
                                             |
                                       _cons |   9.264398   13.73028     0.67   0.508    -19.47342    38.00221
                                ------------------------------------------------------------------------------
                                Instruments corresponding to the linear moment conditions:
                                 1, model(diff):
                                   L2.gdpgrow L2.inflation L2.gfcfgrow L2.hfcegrow
                                 2, model(level):
                                   L1.D.gdpgrow L1.D.inflation L1.D.gfcfgrow L1.D.hfcegrow
                                 3, model(level):
                                   smei4
                                 4, model(level):
                                   _cons

                                Comment

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