Announcement

Collapse
No announcement yet.
X
  • Filter
  • Time
  • Show
Clear All
new posts

  • Equation Specification xtabond2

    Dear all,

    I'm using xtabond2 program to run a System-GMM regression but due to my lack of experience in estimating such a kind of model I'm not sure if I'm doing this correctly.

    To summarize, my dependent variable is I (co2 emissions) and my regressors are Lagged I, P (population), Arpc (per capita GDP), T (technology), Jovens (% of the young population), Urb (% of the urbanized population). I'm also controlling for time fixed effects and to do that I have manually created time dummies for each period of 11 years (year1-year11). Moreover, I'm considering P, Arpc and T as endogenous on the other hand Jovens and Urb as exogenous.

    Then I've wrtitten:
    Code:
    xtabond2  L(0/1).I P Arpc T Jovens Urb year3-year11, gmm( (L.I P Arpc T ), laglimits(1 2) collapse equation(diff)) gmm( (L.I P Arpc T), laglimits (1 2) collapse eq(level)) ivstyle(year3-year11 Jovens Urb, eq(level)) robust
    Output:
    Code:
    Warning: Two-step estimated covariance matrix of moments is singular.
    Using a generalized inverse to calculate robust weighting matrix for Hansen test.
    Difference-in-Sargan/Hansen statistics may be negative.
    
    Dynamic panel-data estimation, one-step system GMM
    
    Group variable: Ufs                             Number of obs      =       243
    Time variable : Year                        Number of groups   =        27
    Number of instruments = 27                      Obs per group: min =         9
    Wald chi2(15) =  12631.21                                      avg =      9.00
    Prob > chi2   =     0.000                                      max =         9
    
    Robust
    I       Coef.   Std. Err.      z    P>z     [95% Conf. Interval]
    
    I
    L1.    .6907989   .1206875     5.72   0.000     .4542556    .9273421
                
    P    .3544818   .1373551     2.58   0.010     .0852707    .6236929
    Arpc    .0193619   .1761628     0.11   0.912    -.3259108    .3646347
    T    .2768321   .1494431     1.85   0.064     -.016071    .5697353
    Jovens    .5500081   .6569872     0.84   0.402    -.7376632    1.837679
    Urb    .1433756   .3570893     0.40   0.688    -.5565066    .8432577
    year3    .1007848   .0325687     3.09   0.002     .0369514    .1646182
    year4    .0568161   .0276051     2.06   0.040      .002711    .1109211
    year5    .0044997   .0342335     0.13   0.895    -.0625967    .0715962
    year6           0  (omitted)
    year7   -.0018433   .0437424    -0.04   0.966    -.0875769    .0838903
    year8    .0895687    .047629     1.88   0.060    -.0037824    .1829197
    year9    .0757619   .0492587     1.54   0.124    -.0207834    .1723072
    year10    .0882288   .0575049     1.53   0.125    -.0244787    .2009363
    year11   -.0233054   .0688169    -0.34   0.735     -.158184    .1115732
    _cons   -2.485526    3.38637    -0.73   0.463    -9.122689    4.151637
    
    Instruments for first differences equation
    GMM-type (missing=0, separate instruments for each period unless collapsed)
    L(1/2).(L.I P Arpc T) collapsed
    Instruments for levels equation
    Standard
    year3 year4 year5 year6 year7 year8 year9 year10 year11 Jovens Urb
    _cons
    GMM-type (missing=0, separate instruments for each period unless collapsed)
    DL(1/2).(L.I P Arpc T) collapsed
    
    Arellano-Bond test for AR(1) in first differences: z =  -2.02  Pr > z =  0.043
    Arellano-Bond test for AR(2) in first differences: z =  -0.05  Pr > z =  0.958
    
    Sargan test of overid. restrictions: chi2(11)   =  21.87  Prob > chi2 =  0.025
    (Not robust, but not weakened by many instruments.)
    Hansen test of overid. restrictions: chi2(11)   =   9.95  Prob > chi2 =  0.535
    (Robust, but weakened by many instruments.)
    
    Difference-in-Hansen tests of exogeneity of instrument subsets:
    GMM instruments for levels
    Hansen test excluding group:     chi2(3)    =   4.92  Prob > chi2 =  0.177
    Difference (null H = exogenous): chi2(8)    =   5.03  Prob > chi2 =  0.754
    gmm(L.I P Arpc T, collapse eq(diff) lag(1 2))
    Hansen test excluding group:     chi2(3)    =   6.95  Prob > chi2 =  0.074
    Difference (null H = exogenous): chi2(8)    =   3.00  Prob > chi2 =  0.934
    gmm(L.I P Arpc T, collapse eq(level) lag(1 2))
    Hansen test excluding group:     chi2(3)    =   4.92  Prob > chi2 =  0.177
    Difference (null H = exogenous): chi2(8)    =   5.03  Prob > chi2 =  0.754
    iv(year3 year4 year5 year6 year7 year8 year9 year10 year11 Jovens Urb, eq(level))
    Hansen test excluding group:     chi2(1)    =   0.50  Prob > chi2 =  0.482
    Difference (null H = exogenous): chi2(10)   =   9.46  Prob > chi2 =  0.489


    Question:
    1) Is my equation correctly specified in terms of what I want to estimate?
    2) In the two-step estimate case, is Windmeijer (2005) finite sample corrected standard errors automatically considered?

    Thanks in advance
    Last edited by Fillipe Soares; 10 Aug 2018, 21:06.

  • #2
    1) One of your time dummies was omitted due to the dynamic nature of the model. You should only specify them as year4-year11 to avoid incorrect degrees of freedom for the overidentification tests.
    2) The Windmeijer correction is automatically applied when you combine the twostep and the robust options.

    Comment


    • #3
      Thank you very much again, Dr. Kripfganz

      Besides the incorrect time dummies specification, is there any other issue in my equation? Especially about the way I have done to define my endogenous and exogenous variables.

      Comment


      • #4
        You have specified your instruments currently as if variables P Arpc T are predetermined. If they are supposed to be endogenous, you would need to specify the following instead:
        Code:
        xtabond2  L(0/1).I P Arpc T Jovens Urb year4-year11, gmm(L.I, laglimits(1 2) collapse equation(diff)) gmm(P Arpc T, laglimits(2 3) collapse equation(diff)) gmm(L.I, laglimits(0 1) collapse eq(level)) gmm(P Arpc T, laglimits(1 2) collapse eq(level)) ivstyle(year4-year11 Jovens Urb, eq(level)) robust
        Further note that it is unusual to specify more than one lag in the gmm option for the level equation (although this is not necessarily incorrect).

        Comment


        • #5
          Dr. Kripfganz, I’m deeply grateful.

          I really appreciate what you have done!

          Comment


          • #6
            Dr. Kripfganz,

            I want to check my results considering my variable Jovens as predetermined.

            Is my code below appropriated?

            Code:
            xtabond2  L(0/1).I P Arpc T Jovens ano4-ano11, gmm(L.I, laglimits(1 2) collapse equation(diff)) gmm(Jovens, laglimits(1 2) collapse equation(diff)) gmm(P Arpc T, laglimits(1 2) collapse equation(diff)) gmm(Jovens, laglimits(1 2) collapse equation(level)) gmm(L.I, laglimits(0 1) collapse eq(level)) gmm(P Arpc T, laglimits(1 2) collapse eq(level)) ivstyle(ano4-ano11, eq(level)) twostep small robust
            Last edited by Fillipe Soares; 14 Aug 2018, 13:11.

            Comment

            Working...
            X