I am working with the xtabond2 command in Stata to solve the endogeneity problem of my estimation. I read the construction of doing xtabond2 from David Roodman. However, I am still confused that my coding is right or wrong. I appreciate receiving your advice :
Code:
*************: xtabond2 ltotalfertility l.ltotalfertility l(0/2).(pand_res_pt) i.year, /// iv(i.year, eq(both)) /// iv(l(0/2).(pand_res_pt), eq(both)) /// gmm(ltotalfertility, lag(2 .) collapse eq(both)) /// h(1) ar(3) two cluster(ifscode)
Code:
. xtabond2 ltotalfertility l.ltotalfertility l(0/2).(pand_res_pt) i.year, ///
> iv(i.year, eq(both)) ///
> iv(l(0/2).(pand_res_pt), eq(both)) ///
> gmm(ltotalfertility, lag(2 .) collapse eq(both)) ///
> h(1) ar(3) two cluster(ifscode)
Favoring speed over space. To switch, type or click on mata: mata set matafavor space, perm.
1996b.year dropped due to collinearity
1997.year dropped due to collinearity
2009.year dropped due to collinearity
2018.year dropped due to collinearity
2019.year dropped due to collinearity
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/Hansen statistics may be negative.
Dynamic panel-data estimation, two-step system GMM
------------------------------------------------------------------------------
Group variable: ifscode Number of obs = 3622
Time variable : year Number of groups = 183
Number of instruments = 44 Obs per group: min = 1
Wald chi2(23) = 69569.39 avg = 19.79
Prob > chi2 = 0.000 max = 20
(Std. err. adjusted for clustering on ifscode)
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| Corrected
ltotalfertility | Coefficient std. err. z P>|z| [95% conf. interval]
----------------+----------------------------------------------------------------
ltotalfertility |
L1. | .8861639 .0218916 40.48 0.000 .8432571 .9290708
|
pand_res_pt |
--. | -.0253932 .0059697 -4.25 0.000 -.0370936 -.0136929
L1. | -.0309516 .008387 -3.69 0.000 -.0473897 -.0145134
L2. | -.0282224 .0065436 -4.31 0.000 -.0410476 -.0153972
|
year |
1998 | -.0060163 .0048398 -1.24 0.214 -.0155022 .0034696
1999 | -.0054971 .004538 -1.21 0.226 -.0143914 .0033972
2000 | -.0053192 .0040945 -1.30 0.194 -.0133442 .0027059
2001 | -.0116792 .004663 -2.50 0.012 -.0208184 -.0025399
2002 | -.0085614 .0040365 -2.12 0.034 -.0164728 -.00065
2003 | -.0082516 .0035831 -2.30 0.021 -.0152744 -.0012288
2004 | -.0066783 .0034789 -1.92 0.055 -.0134968 .0001401
2005 | -.0094819 .0034326 -2.76 0.006 -.0162097 -.0027541
2006 | -.006441 .0031204 -2.06 0.039 -.0125569 -.0003251
2007 | -.0062036 .0032063 -1.93 0.053 -.0124879 .0000806
2008 | -.006044 .0029945 -2.02 0.044 -.0119131 -.0001749
2010 | .0023753 .0019727 1.20 0.229 -.0014911 .0062418
2011 | -.0024552 .0013919 -1.76 0.078 -.0051833 .0002729
2012 | -.0121593 .0034885 -3.49 0.000 -.0189966 -.005322
2013 | -.0177037 .0037538 -4.72 0.000 -.0250609 -.0103465
2014 | -.0139368 .0039577 -3.52 0.000 -.0216938 -.0061797
2015 | -.0176519 .0041448 -4.26 0.000 -.0257756 -.0095281
2016 | -.018251 .0041753 -4.37 0.000 -.0264344 -.0100676
2017 | -.0233959 .0044382 -5.27 0.000 -.0320946 -.0146973
|
_cons | .1172786 .0240108 4.88 0.000 .0702183 .1643389
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Instruments for first differences equation
Standard
D.(pand_res_pt L.pand_res_pt L2.pand_res_pt)
D.(1996b.year 1997.year 1998.year 1999.year 2000.year 2001.year 2002.year
2003.year 2004.year 2005.year 2006.year 2007.year 2008.year 2009.year
2010.year 2011.year 2012.year 2013.year 2014.year 2015.year 2016.year
2017.year 2018.year 2019.year)
GMM-type (missing=0, separate instruments for each period unless collapsed)
L(2/23).ltotalfertility collapsed
Instruments for levels equation
Standard
pand_res_pt L.pand_res_pt L2.pand_res_pt
1996b.year 1997.year 1998.year 1999.year 2000.year 2001.year 2002.year
2003.year 2004.year 2005.year 2006.year 2007.year 2008.year 2009.year
2010.year 2011.year 2012.year 2013.year 2014.year 2015.year 2016.year
2017.year 2018.year 2019.year
_cons
GMM-type (missing=0, separate instruments for each period unless collapsed)
DL.ltotalfertility collapsed
------------------------------------------------------------------------------
Arellano-Bond test for AR(1) in first differences: z = -4.49 Pr > z = 0.000
Arellano-Bond test for AR(2) in first differences: z = 0.67 Pr > z = 0.501
Arellano-Bond test for AR(3) in first differences: z = -1.82 Pr > z = 0.069
------------------------------------------------------------------------------
Sargan test of overid. restrictions: chi2(20) = 17.39 Prob > chi2 = 0.628
(Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(20) = 96.22 Prob > chi2 = 0.000
(Robust, but weakened by many instruments.)
Difference-in-Hansen tests of exogeneity of instrument subsets:
GMM instruments for levels
Hansen test excluding group: chi2(19) = 70.91 Prob > chi2 = 0.000
Difference (null H = exogenous): chi2(1) = 25.31 Prob > chi2 = 0.000
iv(1996b.year 1997.year 1998.year 1999.year 2000.year 2001.year 2002.year 2003.year 2004.year 2005.year 2006.year 2007.year 2008.year 2009
> .year 2010.year 2011.year 2012.year 2013.year 2014.year 2015.year 2016.year 2017.year 2018.year 2019.year)
Hansen test excluding group: chi2(1) = 0.10 Prob > chi2 = 0.757
Difference (null H = exogenous): chi2(19) = 96.12 Prob > chi2 = 0.000
iv(pand_res_pt L.pand_res_pt L2.pand_res_pt)
Hansen test excluding group: chi2(17) = 90.00 Prob > chi2 = 0.000
Difference (null H = exogenous): chi2(3) = 6.21 Prob > chi2 = 0.102
.
end of do-file***also I used the "xtdpdgmm" to get te better results, I did this :
Code:
xtdpdgmm L(0/1).ltotalfertility l(0/2).(pand_res_pt) i.year, noserial gmmiv(L.ltotalfertility, collapse model(difference)) iv(l(0/2).(pand_res_pt) i.year, difference model(difference)) twostep vce(robust)
Code:
. xtdpdgmm L(0/1).ltotalfertility l(0/2).(pand_res_pt) i.year, noserial gmmiv(L.ltotalfertility, collapse model(difference)) iv(l(0/2).(pand
> _res_pt) i.year, difference model(difference)) twostep vce(robust)
note: 1996.year identifies no observations in the sample.
note: 1997.year identifies no observations in the sample.
note: 2017.year omitted because of collinearity.
note: 2018.year identifies no observations in the sample.
note: 2019.year identifies no observations in the sample.
Generalized method of moments estimation
Fitting full model:
Step 1:
initial: f(b) = 19.115759
alternative: f(b) = 4.6514905
rescale: f(b) = .08442007
Iteration 0: f(b) = .08442007
Iteration 1: f(b) = .00083005
Iteration 2: f(b) = .00082701
Iteration 3: f(b) = .00082701
Step 2:
Iteration 0: f(b) = .97149141
Iteration 1: f(b) = .57064682
Iteration 2: f(b) = .56335002
Iteration 3: f(b) = .5633197
Iteration 4: f(b) = .56331884
Iteration 5: f(b) = .56331881
Group variable: ifscode Number of obs = 3622
Time variable: year Number of groups = 183
Moment conditions: linear = 43 Obs per group: min = 1
nonlinear = 18 avg = 19.79235
total = 61 max = 20
(Std. err. adjusted for 183 clusters in ifscode)
---------------------------------------------------------------------------------
| WC-Robust
ltotalfertility | Coefficient std. err. z P>|z| [95% conf. interval]
----------------+----------------------------------------------------------------
ltotalfertility |
L1. | 1.025479 .0123817 82.82 0.000 1.001211 1.049746
|
pand_res_pt |
--. | .0027941 .0027985 1.00 0.318 -.0026909 .0082791
L1. | .0014515 .0040002 0.36 0.717 -.0063887 .0092917
L2. | .0011745 .0039944 0.29 0.769 -.0066543 .0090033
|
year |
1996 | 0 (empty)
1997 | 0 (empty)
1998 | -.0134633 .0039956 -3.37 0.001 -.0212946 -.005632
1999 | -.0099362 .0039732 -2.50 0.012 -.0177235 -.0021489
2000 | -.0072415 .0039882 -1.82 0.069 -.0150582 .0005752
2001 | -.0112745 .0031739 -3.55 0.000 -.0174952 -.0050537
2002 | -.0051081 .0031922 -1.60 0.110 -.0113647 .0011486
2003 | -.0028348 .0032195 -0.88 0.379 -.0091448 .0034752
2004 | .0006642 .0032341 0.21 0.837 -.0056745 .007003
2005 | -.0004954 .002737 -0.18 0.856 -.0058598 .0048691
2006 | .004047 .0031755 1.27 0.202 -.0021767 .0102708
2007 | .0055304 .0032534 1.70 0.089 -.0008462 .0119071
2008 | .0068074 .0032675 2.08 0.037 .0004032 .0132115
2009 | .0009863 .0024508 0.40 0.687 -.0038171 .0057897
2010 | .0030281 .0027427 1.10 0.270 -.0023475 .0084037
2011 | .0013373 .0022822 0.59 0.558 -.0031357 .0058103
2012 | .0048407 .0023568 2.05 0.040 .0002214 .0094599
2013 | .00075 .0015893 0.47 0.637 -.0023651 .003865
2014 | .0057334 .0019981 2.87 0.004 .0018172 .0096496
2015 | .0034253 .0013914 2.46 0.014 .0006982 .0061525
2016 | .003241 .0012295 2.64 0.008 .0008312 .0056508
2017 | 0 (omitted)
2018 | 0 (empty)
2019 | 0 (empty)
|
_cons | -.037772 .0117266 -3.22 0.001 -.0607558 -.0147882
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Instruments corresponding to the linear moment conditions:
1, model(diff):
L1.L.ltotalfertility L2.L.ltotalfertility L3.L.ltotalfertility
L4.L.ltotalfertility L5.L.ltotalfertility L6.L.ltotalfertility
L7.L.ltotalfertility L8.L.ltotalfertility L9.L.ltotalfertility
L10.L.ltotalfertility L11.L.ltotalfertility L12.L.ltotalfertility
L13.L.ltotalfertility L14.L.ltotalfertility L15.L.ltotalfertility
L16.L.ltotalfertility L17.L.ltotalfertility L18.L.ltotalfertility
L19.L.ltotalfertility L20.L.ltotalfertility
2, model(diff):
D.pand_res_pt D.L.pand_res_pt D.L2.pand_res_pt D.1999bn.year D.2000.year
D.2001.year D.2002.year D.2003.year D.2004.year D.2005.year D.2006.year
D.2007.year D.2008.year D.2009.year D.2010.year D.2011.year D.2012.year
D.2013.year D.2014.year D.2015.year D.2016.year D.2017.year
3, model(level):
_cons
.
end of do-filethe results are not the same, Please guide me if is it the right command.
Many thanks in advance for your valuable time and advice.
Regards,
0 Response to Xtabond2 for system GMM. Please help me for coding
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