Thank you for your help. Does it look better with one more lags like these?
Code:
. xtdpdgmm gdpgrow sme inflation gfcfgrow hfcegrow tradeopen l.lrgdpopc ,gmm(gdpgrow inflation gfcfgrow hfcegr > ow lrgdpopc , lag(2 3) collapse model(diff)) gmm(gdpgrow inflation gfcfgrow hfcegrow lrgdpopc, lag(1 2) diff > collapse model(level)) iv(sme,model(level)) one vce(cl id) small overid Generalized method of moments estimation Fitting full model: Step 1 f(b) = 6.5119593 Fitting reduced model 1: Step 1 f(b) = 1.7495734 Fitting reduced model 2: Step 1 f(b) = 2.0088941 Fitting reduced model 3: Step 1 f(b) = 6.4917358 Fitting no-level model: Step 1 f(b) = 2.0088941 Group variable: id Number of obs = 919 Time variable: year Number of groups = 21 Moment conditions: linear = 22 Obs per group: min = 6 nonlinear = 0 avg = 43.7619 total = 22 max = 46 (Std. err. adjusted for 21 clusters in id) ------------------------------------------------------------------------------ | Robust gdpgrow | Coefficient std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- sme | .9128761 .2466531 3.70 0.001 .3983667 1.427386 inflation | -.1421567 .0417887 -3.40 0.003 -.2293263 -.0549871 gfcfgrow | .1127319 .0368393 3.06 0.006 .0358865 .1895773 hfcegrow | .6901123 .1433662 4.81 0.000 .3910556 .989169 tradeopen | -.0239375 .0116199 -2.06 0.053 -.0481762 .0003012 | lrgdpopc | L1. | -.8863398 .8513687 -1.04 0.310 -2.662264 .8895842 | _cons | 10.84036 8.734277 1.24 0.229 -7.379021 29.05974 ------------------------------------------------------------------------------ Instruments corresponding to the linear moment conditions: 1, model(diff): L2.gdpgrow L3.gdpgrow L2.inflation L3.inflation L2.gfcfgrow L3.gfcfgrow L2.hfcegrow L3.hfcegrow L2.lrgdpopc L3.lrgdpopc 2, model(level): L1.D.gdpgrow L2.D.gdpgrow L1.D.inflation L2.D.inflation L1.D.gfcfgrow L2.D.gfcfgrow L1.D.hfcegrow L2.D.hfcegrow L1.D.lrgdpopc L2.D.lrgdpopc 3, model(level): sme 4, model(level): _cons
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