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  • #16
    You can use the estat overid postestimation command directly after running your xtdpdgmm regression. This will provide you with Hansen's J test for all overidentifying restrictions. If you wish to obtain difference-in-Hansen tests, you will have to run regressions for the restricted and unrestricted models, store the estimation results from the first regression under a name and then use estat overid name after the second regression.

    As an aside: You are using the option gmmiv(varlist, difference) which creates first differences of the instruments for the level equation. If that is what you intend, please just ignore this comment. If instead you aim to obtain instruments for the first-differenced equation, the correct syntax would be gmmiv(varlist, model(difference)).

    Comment


    • #17
      Dear Sebastian, thank you for the observation and I have effected the changes as follow:

      Code:
      xtdpdgmm roa l.roa indp cduality bdiversity1 lbsize acindp acfexpr ncindp ccindp lshare  size leverage rdsales cexp netsalesgrw  lage d2006-d2015 d3-d8 , noserial gmmiv(roa indp cduality bdiversity1 lbsize acindp acfexpr ncindp ccindp lshare  size leverage rdsales cexp netsalesgrw ,lag(3 4) model(difference)collapse) iv(d2006-d2015 d3-d8  lage) twostep vce(robust)

      Comment


      • #18
        Dear Sebastian, Please what is the right syntax for the difference-in-Hansen tests for the restricted and unrestricted models. I have gone through the stata manual by Christopher F Baum but I still cannot find the right command to use. For instance I do not know if this should be the right way for the restricted model
        Code:
        ivregress 2sls roa size leverage rdsales lage cexp netsalesgrw i.year i.country i.siccode (indp cduality bdiversity1 lbsize acindp acfexpr ccindp ncindp lshare = l.indp l.cduality l.bdiversity1 l.lbsize l.acindp l.acfexpr l.ccindp l.ncindp l.lshare)
        Code:
        predict u2, resid

        Comment


        • #19
          If you want to use the postestimation command estat overid after xtdpdgmm, you need to estimate both models with xtdpdgmm. You then need to decide which subset of instruments you want to exclude for the testing purposes. For example, if you want to test the validity of the overidentifying restrictions implied by the nonlinear moment conditions, you would run the estimation once with and once without the noserial option, store the first estimation results under a name and then run estat overid name after the second estimation. Here is an example:
          Code:
          . webuse abdata
          . xtdpdgmm L(0/1).n w k, gmmiv(L.n, c m(d)) iv(w k, d m(d)) twostep vce(robust) noserial
          . estimates store gmm1
          . xtdpdgmm L(0/1).n w k, gmmiv(L.n, c m(d)) iv(w k, d m(d)) twostep vce(robust)
          . estat overid gmm1

          Comment


          • #20
            Dear Sebastian, thank you i now understand it better. I have another question as regards how many lags I have to specify for the dynamic completeness of my model. For instance I ran regress
            Code:
            xi: reg roa l.roa l2.roa l3.roa l4.roa  size leverage rdsales lage cexp netsalesgrw i.year i.country, cluster (id) robust
            i.year            _Iyear_2004-2015    (naturally coded; _Iyear_2004 omitted)
            i.country         _Icountry_1-8       (naturally coded; _Icountry_1 omitted)
            note: _Iyear_2005 omitted because of collinearity
            note: _Iyear_2006 omitted because of collinearity
            note: _Iyear_2007 omitted because of collinearity
            note: _Iyear_2015 omitted because of collinearity
            
            Linear regression                               Number of obs     =      4,311
                                                            F(24, 662)        =     103.29
                                                            Prob > F          =     0.0000
                                                            R-squared         =     0.4832
                                                            Root MSE          =     5.4524
            
                                               (Std. Err. adjusted for 663 clusters in id)
            ------------------------------------------------------------------------------
                         |               Robust
                     roa |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
            -------------+----------------------------------------------------------------
                     roa |
                     L1. |    .461267   .0291797    15.81   0.000     .4039711     .518563
                     L2. |   .0822153   .0234362     3.51   0.000     .0361971    .1282335
                     L3. |   .0609813   .0211912     2.88   0.004     .0193712    .1025914
                     L4. |   .0490574   .0211257     2.32   0.021     .0075759    .0905389
                         |
                    size |   .4479199   .0790612     5.67   0.000     .2926789    .6031609
                leverage |  -.0328898   .0056083    -5.86   0.000     -.043902   -.0218777
                 rdsales |  -.0598592   .0154392    -3.88   0.000     -.090175   -.0295435
                    lage |   .0957258    .172054     0.56   0.578    -.2421115    .4335631
                    cexp |  -.0063025   .0258475    -0.24   0.807    -.0570554    .0444504
             netsalesgrw |   .1076629   .0097653    11.03   0.000     .0884882    .1268375
             _Iyear_2005 |          0  (omitted)
             _Iyear_2006 |          0  (omitted)
             _Iyear_2007 |          0  (omitted)
             _Iyear_2008 |  -1.436325   .4090706    -3.51   0.000    -2.239557   -.6330932
             _Iyear_2009 |   .4156977   .3655749     1.14   0.256    -.3021283    1.133524
             _Iyear_2010 |   1.649389   .3221515     5.12   0.000     1.016828    2.281951
             _Iyear_2011 |   .6066941   .3311038     1.83   0.067    -.0434461    1.256834
             _Iyear_2012 |   .7450445   .3074329     2.42   0.016     .1413834    1.348706
             _Iyear_2013 |   .8284794   .3015524     2.75   0.006      .236365    1.420594
             _Iyear_2014 |   .9488653   .2828824     3.35   0.001     .3934104     1.50432
             _Iyear_2015 |          0  (omitted)
             _Icountry_2 |  -.1262503    .296474    -0.43   0.670    -.7083931    .4558925
             _Icountry_3 |  -1.051163   .3102917    -3.39   0.001    -1.660438   -.4418889
             _Icountry_4 |  -.1288008   .5198947    -0.25   0.804    -1.149642    .8920404
             _Icountry_5 |  -1.456031   .2511707    -5.80   0.000    -1.949218   -.9628439
             _Icountry_6 |  -1.326935   .4667966    -2.84   0.005    -2.243515   -.4103542
             _Icountry_7 |    -.11488    1.02714    -0.11   0.911    -2.131724    1.901964
             _Icountry_8 |  -.5622005   .3524436    -1.60   0.111    -1.254243    .1298416
                   _cons |  -1.615194   .9655349    -1.67   0.095    -3.511074    .2806858
            my question is does that mean I have to specify dynamic completeness using the first four lags of dependent variable and run the xtdpdgmm as

            Code:
             
             xtdpdgmm l(0/4) indp cduality bdiversity1 lbsize acindp acfexpr ncindp ccindp lshare  size leverage rdsales cexp netsalesgrw  lage d2006-d2015 d3-d8 , noserial gmmiv(roa indp cduality bdiversity1 lbsize acindp acfexpr ncindp ccindp lshare  size leverage rdsales cexp netsalesgrw ,lag(4 .) model(difference)collapse) iv(d2006-d2015 d3-d8  lage) twostep vce(robust)

            Comment


            • #21
              What you want to achieve is that the errors of your regression are serially uncorrelated. After running an xtdpdgmm regression, you can use the Arellano-Bond test for serial correlation in the first-differenced residuals. If you are rejecting the null hypothesis of no serial correlation for order 2 or higher, this indicates that your model is not dynamically complete. This test is obtained with the postestimation command estat serial. For details, please see
              Code:
              help xtdpdgmm postestimation

              Comment


              • #22
                Yes I understand what the estat serial does. In terms of the dynamic completeness I am asking, I am referring to the seminar work of Wintoki et al (2012) : Endogeneity and the dynamics of internal corporate governance. http://www.sciencedirect.com/science...04405X12000426

                on page: 593 section 5.1. They made mention of how many lags of performance are needed to ensure dynamic completeness of the model. where they ran regress lags of performance on the control variables and determined that for their case 2 lags of performance will be relevant and they showed in a Table 4 on page 594. They therefore estimated their model using :
                Code:
                Xi: xtabond2 roa l.roa l2.roa..........etc
                --- 2 lags of performance to control for dynamic completness and in the gmmstyle they use lag(3 4)

                My question from before is should I include four lags on my model as well for dynamic completeness in my model because from my initial table, lag four is still significant. It was only on lag 5 that Roa became insignificant


                .
                Code:
                 xi: reg roa l.roa l2.roa l3.roa l4.roa l5.roa  size leverage rdsales lage cexp netsalesgrw i.year i.country, cluster (id) robust
                i.year            _Iyear_2004-2015    (naturally coded; _Iyear_2004 omitted)
                i.country         _Icountry_1-8       (naturally coded; _Icountry_1 omitted)
                note: _Iyear_2005 omitted because of collinearity
                note: _Iyear_2006 omitted because of collinearity
                note: _Iyear_2007 omitted because of collinearity
                note: _Iyear_2008 omitted because of collinearity
                note: _Iyear_2013 omitted because of collinearity
                
                Linear regression                               Number of obs     =      3,703
                                                                F(24, 656)        =      77.11
                                                                Prob > F          =     0.0000
                                                                R-squared         =     0.5086
                                                                Root MSE          =     5.0871
                
                                                   (Std. Err. adjusted for 657 clusters in id)
                ------------------------------------------------------------------------------
                             |               Robust
                         roa |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
                -------------+----------------------------------------------------------------
                         roa |
                         L1. |   .4559014   .0290334    15.70   0.000     .3988918    .5129111
                         L2. |   .0741202   .0232036     3.19   0.001     .0285578    .1196825
                         L3. |    .067437   .0202817     3.33   0.001     .0276122    .1072618
                         L4. |   .0504379   .0196522     2.57   0.010     .0118491    .0890267
                         L5. |   .0148837   .0184981     0.80   0.421    -.0214389    .0512062
                             |
                        size |   .3857348   .0817339     4.72   0.000     .2252431    .5462265
                    leverage |  -.0258838   .0059378    -4.36   0.000    -.0375431   -.0142244
                     rdsales |  -.0461942   .0176924    -2.61   0.009    -.0809347   -.0114537
                        lage |   .1694536   .1843043     0.92   0.358     -.192444    .5313511
                        cexp |  -.0155643   .0285172    -0.55   0.585    -.0715603    .0404318
                 netsalesgrw |   .1058518   .0102332    10.34   0.000      .085758    .1259456
                 _Iyear_2005 |          0  (omitted)
                 _Iyear_2006 |          0  (omitted)
                 _Iyear_2007 |          0  (omitted)
                 _Iyear_2008 |          0  (omitted)
                 _Iyear_2009 |   -.480308   .3501509    -1.37   0.171     -1.16786    .2072436
                 _Iyear_2010 |   .7967455   .3160692     2.52   0.012     .1761161    1.417375
                 _Iyear_2011 |  -.2369184   .3192528    -0.74   0.458    -.8637989    .3899621
                 _Iyear_2012 |  -.0985054   .3259502    -0.30   0.763    -.7385369    .5415262
                 _Iyear_2013 |          0  (omitted)
                 _Iyear_2014 |   .1176404   .3000777     0.39   0.695    -.4715881    .7068689
                 _Iyear_2015 |  -.8743362   .2996419    -2.92   0.004    -1.462709   -.2859633
                 _Icountry_2 |  -.3152732   .2915884    -1.08   0.280    -.8878324    .2572859
                 _Icountry_3 |  -1.467921   .3030947    -4.84   0.000    -2.063074   -.8727683
                 _Icountry_4 |   -.378966   .5387304    -0.70   0.482     -1.43681     .678878
                 _Icountry_5 |  -1.565008   .2970455    -5.27   0.000    -2.148282   -.9817331
                 _Icountry_6 |  -1.251977   .4520147    -2.77   0.006    -2.139547   -.3644069
                 _Icountry_7 |  -.0683118   1.143688    -0.06   0.952    -2.314042    2.177418
                 _Icountry_8 |  -.3626713   .4779429    -0.76   0.448    -1.301154    .5758111
                       _cons |  -.7178755   .9767178    -0.73   0.463    -2.635746    1.199995
                ------------------------------------------------------------------------------
                
                .
                So should my model be
                Code:
                 
                 xtdpdgmm l(0/4).roa indp cduality bdiversity1 lbsize acindp acfexpr ncindp ccindp lshare  size leverage rdsales cexp netsalesgrw  lage d2006-d2015 d3-d8 , noserial gmmiv(roa indp cduality bdiversity1 lbsize acindp acfexpr ncindp ccindp lshare  size leverage rdsales cexp netsalesgrw ,lag(5 .) model(difference)collapse) iv(d2006-d2015 d3-d8  lage) twostep vce(robust)
                or

                Code:
                 
                 xtdpdgmm roa l.roa l2.roa l3.roa l4.roa indp cduality bdiversity1 lbsize acindp acfexpr ncindp ccindp lshare  size leverage rdsales cexp netsalesgrw  lage d2006-d2015 d3-d8 , noserial gmmiv(roa indp cduality bdiversity1 lbsize acindp acfexpr ncindp ccindp lshare  size leverage rdsales cexp netsalesgrw ,lag(5 .) model(difference)collapse) iv(d2006-d2015 d3-d8  lage) twostep vce(robust)

                Comment


                • #23
                  If your fourth lag in the GMM specification is statistically significant, then you probably want to keep it. This consideration is related to testing for serial correlation of the errors, because dropping such a significant lag is likely to create serially correlated errors.

                  Comment

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