Hi guys,
i'm using STATA 13 to estimate my dyamic panel model. The aim of my thesis is to understand if globalization affects economic growth. I start from a Fixed effects model and I want to spve the endogeneity problem. Since i use a growth model, someone suggests me to use a dynamic one. I use a 5-years panel and the variables are the average over the 5 years. My dependent variable is the GDP growth rate and the regressors are different globalization indices that I used one by one, plus several macroeconomic variables used like controls, I show you an example with one of them. I add time dummies in order to take into account time effects (I add them one by one as suggested in this Forum).
GDPgrowth_5 lnFertility_5 newinvest newGen_exp newoglob are the endogenous variables
newlsc lnLife_5 are the predetermined variables
I have the following questions:
1 - I tried to avoid the proliferation of instruments using only two lags but I obtain a warning and I don't know if it is related to this problem.
2 - I tried to change globalization indices but the GDP lagged is never significant. This may suggest that a static model using instrumental variables like xtivreg2 (but in this case I have to fins instruments) may better fit to my data to solve the endogeneity problem? Or alternatively, it might make sense to run an AB model without the dependent lagged variable to make it a static model?
I would appreciate some feedbacks (also to my AB model, I'm not 100% sure I've used the AB options well)
Thank you!
i'm using STATA 13 to estimate my dyamic panel model. The aim of my thesis is to understand if globalization affects economic growth. I start from a Fixed effects model and I want to spve the endogeneity problem. Since i use a growth model, someone suggests me to use a dynamic one. I use a 5-years panel and the variables are the average over the 5 years. My dependent variable is the GDP growth rate and the regressors are different globalization indices that I used one by one, plus several macroeconomic variables used like controls, I show you an example with one of them. I add time dummies in order to take into account time effects (I add them one by one as suggested in this Forum).
GDPgrowth_5 lnFertility_5 newinvest newGen_exp newoglob are the endogenous variables
newlsc lnLife_5 are the predetermined variables
Code:
xtabond2 GDPgrowth_5 l.GDPgrowth_5 newoglob iGDP newlsc lnLife_5 lnFertility_5 newinvest newGen_exp newInfl2 trade newdbagdp dem_5 y70 y75 y80 y85 y90 y95 y00 y05, gmm (GDPgrowth_5 lnFertility_5 newinvest newGen_exp newoglob, lag (2 3) eq(diff)) gmm (newlsc lnLife_5, lag (1 2) eq(diff)) iv (newInfl2 dem_5 trade newdbagdp iGDP, eq(diff)) iv(y70 y75 > y80 y85 y90 y95 y00 y05, eq(level)) twostep nolevel robust small ar(3) Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm. Instruments for levels equations only ignored since noleveleq specified. 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 difference GMM ------------------------------------------------------------------------------ Group variable: id2 Number of obs = 224 Time variable : period Number of groups = 69 Number of instruments = 56 Obs per group: min = 0 F(20, 69) = 18.47 avg = 3.25 Prob > F = 0.000 max = 4 ------------------------------------------------------------------------------- | Corrected GDPgrowth_5 | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- GDPgrowth_5 | L1. | -.0932418 .1432099 -0.65 0.517 -.3789377 .1924541 | newoglob | .2020836 .0897642 2.25 0.028 .0230089 .3811584 iGDP | -.104824 .0246037 -4.26 0.000 -.1539071 -.0557409 newlsc | .0821822 .0827541 0.99 0.324 -.0829077 .247272 lnLife_5 | .1667886 .0705606 2.36 0.021 .026024 .3075532 lnFertility_5 | .0314123 .023665 1.33 0.189 -.015798 .0786226 newinvest | .2726478 .0819279 3.33 0.001 .1092061 .4360894 newGen_exp | -.0661485 .0816824 -0.81 0.421 -.2291005 .0968035 newInfl2 | .0081002 .0026686 3.04 0.003 .0027764 .013424 trade | .0010489 .000336 3.12 0.003 .0003785 .0017193 newdbagdp | .005614 .0203926 0.28 0.784 -.0350681 .046296 dem_5 | -.0376581 .0283473 -1.33 0.188 -.0942093 .0188931 y70 | 0 (omitted) y75 | .0095499 .0068016 1.40 0.165 -.004019 .0231188 y80 | 0 (omitted) y85 | .0015786 .0043232 0.37 0.716 -.0070458 .0102031 y90 | -.00236 .0079309 -0.30 0.767 -.0181817 .0134617 y95 | .0027976 .0132303 0.21 0.833 -.0235961 .0291912 y00 | 0 (omitted) y05 | 0 (omitted) ------------------------------------------------------------------------------- Instruments for first differences equation Standard D.(newInfl2 dem_5 trade newdbagdp iGDP) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/2).(newlsc lnLife_5) L(2/3).(GDPgrowth_5 lnFertility_5 newinvest newGen_exp newoglob) ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -2.08 Pr > z = 0.038 Arellano-Bond test for AR(2) in first differences: z = -1.12 Pr > z = 0.264 Arellano-Bond test for AR(3) in first differences: z = -0.09 Pr > z = 0.931 ------------------------------------------------------------------------------ Sargan test of overid. restrictions: chi2(36) = 78.74 Prob > chi2 = 0.000 (Not robust, but not weakened by many instruments.) Hansen test of overid. restrictions: chi2(36) = 43.12 Prob > chi2 = 0.193 (Robust, but weakened by many instruments.) Difference-in-Hansen tests of exogeneity of instrument subsets: gmm(GDPgrowth_5 lnFertility_5 newinvest newGen_exp newoglob, eq(diff) lag(2 3)) Hansen test excluding group: chi2(1) = 4.41 Prob > chi2 = 0.036 Difference (null H = exogenous): chi2(35) = 38.71 Prob > chi2 = 0.306 gmm(newlsc lnLife_5, eq(diff) lag(1 2)) Hansen test excluding group: chi2(20) = 26.00 Prob > chi2 = 0.166 Difference (null H = exogenous): chi2(16) = 17.12 Prob > chi2 = 0.378 iv(newInfl2 dem_5 trade newdbagdp iGDP, eq(diff)) Hansen test excluding group: chi2(31) = 35.96 Prob > chi2 = 0.247 Difference (null H = exogenous): chi2(5) = 7.16 Prob > chi2 = 0.209
1 - I tried to avoid the proliferation of instruments using only two lags but I obtain a warning and I don't know if it is related to this problem.
2 - I tried to change globalization indices but the GDP lagged is never significant. This may suggest that a static model using instrumental variables like xtivreg2 (but in this case I have to fins instruments) may better fit to my data to solve the endogeneity problem? Or alternatively, it might make sense to run an AB model without the dependent lagged variable to make it a static model?
I would appreciate some feedbacks (also to my AB model, I'm not 100% sure I've used the AB options well)
Thank you!
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