Can someone help me check if my results and my code are valid:
. xtabond2 stunting l.stunting ihsodapc l.ihsodapc l2.ihsodapc lngdppc ghe trade inf popgr unemp debt, ///
> gmm(l.stunting l(0/2).ihsodapc ghe l.debt, collapse) iv(l(0/2).(lngdppc) l(0/2).(popgr) inf unemp l(0/2).(trade)
> , eq(level)) ///
> twostep small robust orthogonal
Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm.
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.
DFm
11
Dynamic panel-data estimation, two-step system GMM
Group variable: country_code Number of obs = 1656
Time variable : year Number of groups = 92
Number of instruments = 92 Obs per group: min = 18
F(11, 91) = 18473.67 avg = 18.00
Prob > F = 0.000 max = 18
Corrected
stunting Coef. Std. Err. t P>t [95% Conf. Interval]
stunting
L1. .9703522 .0090228 107.54 0.000 .9524295 .9882749
ihsodapc
-. -.1362257 .0346116 -3.94 0.000 -.2049774 -.067474
L1. -.1126607 .0263474 -4.28 0.000 -.1649966 -.0603248
L2. -.1267187 .032502 -3.90 0.000 -.1912799 -.0621575
lngdppc -.4256417 .1184228 -3.59 0.001 -.660874 -.1904095
ghe .109082 .0516651 2.11 0.037 .0064555 .2117084
trade .004624 .0026081 1.77 0.080 -.0005567 .0098046
inf -.0043612 .002057 -2.12 0.037 -.0084471 -.0002752
popgr .0806829 .0484529 1.67 0.099 -.0155629 .1769286
unemp .0167633 .0104299 1.61 0.111 -.0039544 .037481
debt .0118125 .0026691 4.43 0.000 .0065107 .0171142
_cons 3.623946 1.173597 3.09 0.003 1.292741 5.955152
Instruments for orthogonal deviations equation
GMM-type (missing=0, separate instruments for each period unless collapsed)
L(1/19).(L.stunting ihsodapc L.ihsodapc L2.ihsodapc ghe L.debt) collapsed
Instruments for levels equation
Standard
lngdppc L.lngdppc L2.lngdppc popgr L.popgr L2.popgr inf unemp trade
L.trade L2.trade
_cons
GMM-type (missing=0, separate instruments for each period unless collapsed)
D.(L.stunting ihsodapc L.ihsodapc L2.ihsodapc ghe L.debt) collapsed
Arellano-Bond test for AR(1) in first differences: z = 1.74 Pr > z = 0.082
Arellano-Bond test for AR(2) in first differences: z = 0.89 Pr > z = 0.373
Sargan test of overid. restrictions: chi2(80) =1917.65 Prob > chi2 = 0.000
(Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(80) = 87.48 Prob > chi2 = 0.266
(Robust, but weakened by many instruments.)
Difference-in-Hansen tests of exogeneity of instrument subsets:
GMM instruments for levels
Hansen test excluding group: chi2(74) = 83.03 Prob > chi2 = 0.221
Difference (null H = exogenous): chi2(6) = 4.44 Prob > chi2 = 0.617
gmm(L.stunting ihsodapc L.ihsodapc L2.ihsodapc ghe L.debt, collapse lag(1 .))
Hansen test excluding group: chi2(0) = 0.00 Prob > chi2 = .
Difference (null H = exogenous): chi2(80) = 87.48 Prob > chi2 = 0.266
iv(lngdppc L.lngdppc L2.lngdppc popgr L.popgr L2.popgr inf unemp trade L.trade L2.trade, eq(level))
Hansen test excluding group: chi2(69) = 82.43 Prob > chi2 = 0.129
Difference (null H = exogenous): chi2(11) = 5.04 Prob > chi2 = 0.929
Where my dependent variable is stunting, my main independent variable is ODA per capita (ihsodapc which is the IHS-transformed odapc) and my other variables are government health expenditure (ghe), trade, inflation (inf), population growth rate (popgr), unemployment (unemp), and debt.
Basically our study aims to check the effect of aid on stunting rates among developing countries and we would greatly appreciate any piece of advice to further strengthen and enhance the study's data analysis by either adding more steps to further validate the results of the study or other techniques (usage of graphs, estimating the short-run or long-run effects of aid, and the like) for our study.
. xtabond2 stunting l.stunting ihsodapc l.ihsodapc l2.ihsodapc lngdppc ghe trade inf popgr unemp debt, ///
> gmm(l.stunting l(0/2).ihsodapc ghe l.debt, collapse) iv(l(0/2).(lngdppc) l(0/2).(popgr) inf unemp l(0/2).(trade)
> , eq(level)) ///
> twostep small robust orthogonal
Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm.
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.
DFm
11
Dynamic panel-data estimation, two-step system GMM
Group variable: country_code Number of obs = 1656
Time variable : year Number of groups = 92
Number of instruments = 92 Obs per group: min = 18
F(11, 91) = 18473.67 avg = 18.00
Prob > F = 0.000 max = 18
Corrected
stunting Coef. Std. Err. t P>t [95% Conf. Interval]
stunting
L1. .9703522 .0090228 107.54 0.000 .9524295 .9882749
ihsodapc
-. -.1362257 .0346116 -3.94 0.000 -.2049774 -.067474
L1. -.1126607 .0263474 -4.28 0.000 -.1649966 -.0603248
L2. -.1267187 .032502 -3.90 0.000 -.1912799 -.0621575
lngdppc -.4256417 .1184228 -3.59 0.001 -.660874 -.1904095
ghe .109082 .0516651 2.11 0.037 .0064555 .2117084
trade .004624 .0026081 1.77 0.080 -.0005567 .0098046
inf -.0043612 .002057 -2.12 0.037 -.0084471 -.0002752
popgr .0806829 .0484529 1.67 0.099 -.0155629 .1769286
unemp .0167633 .0104299 1.61 0.111 -.0039544 .037481
debt .0118125 .0026691 4.43 0.000 .0065107 .0171142
_cons 3.623946 1.173597 3.09 0.003 1.292741 5.955152
Instruments for orthogonal deviations equation
GMM-type (missing=0, separate instruments for each period unless collapsed)
L(1/19).(L.stunting ihsodapc L.ihsodapc L2.ihsodapc ghe L.debt) collapsed
Instruments for levels equation
Standard
lngdppc L.lngdppc L2.lngdppc popgr L.popgr L2.popgr inf unemp trade
L.trade L2.trade
_cons
GMM-type (missing=0, separate instruments for each period unless collapsed)
D.(L.stunting ihsodapc L.ihsodapc L2.ihsodapc ghe L.debt) collapsed
Arellano-Bond test for AR(1) in first differences: z = 1.74 Pr > z = 0.082
Arellano-Bond test for AR(2) in first differences: z = 0.89 Pr > z = 0.373
Sargan test of overid. restrictions: chi2(80) =1917.65 Prob > chi2 = 0.000
(Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(80) = 87.48 Prob > chi2 = 0.266
(Robust, but weakened by many instruments.)
Difference-in-Hansen tests of exogeneity of instrument subsets:
GMM instruments for levels
Hansen test excluding group: chi2(74) = 83.03 Prob > chi2 = 0.221
Difference (null H = exogenous): chi2(6) = 4.44 Prob > chi2 = 0.617
gmm(L.stunting ihsodapc L.ihsodapc L2.ihsodapc ghe L.debt, collapse lag(1 .))
Hansen test excluding group: chi2(0) = 0.00 Prob > chi2 = .
Difference (null H = exogenous): chi2(80) = 87.48 Prob > chi2 = 0.266
iv(lngdppc L.lngdppc L2.lngdppc popgr L.popgr L2.popgr inf unemp trade L.trade L2.trade, eq(level))
Hansen test excluding group: chi2(69) = 82.43 Prob > chi2 = 0.129
Difference (null H = exogenous): chi2(11) = 5.04 Prob > chi2 = 0.929
Where my dependent variable is stunting, my main independent variable is ODA per capita (ihsodapc which is the IHS-transformed odapc) and my other variables are government health expenditure (ghe), trade, inflation (inf), population growth rate (popgr), unemployment (unemp), and debt.
Basically our study aims to check the effect of aid on stunting rates among developing countries and we would greatly appreciate any piece of advice to further strengthen and enhance the study's data analysis by either adding more steps to further validate the results of the study or other techniques (usage of graphs, estimating the short-run or long-run effects of aid, and the like) for our study.
