Hi Statalist members,
I am using GMM for my thesis. I have read papers and topics here but I am not sure about my knowledge so I would like to hear from you.
My data has 1 dependent, 8 independent and 4 control variables. I am coding as following:
xtabond2 Adj_TobinQt1 l.Adj_TobinQt1 Firm_age FirmSize TEAMSIZE GENDIV EDLEVDIV EDBGRDIV EDCTRDIV NUMFIRDIV NUMINDDIV INTEXPDIV CERTDIV MEMSDIV y*, gmm( Adj_TobinQt1 , lag (3 5) collapse) gmm( FirmSize TEAMSIZE GENDIV EDLEVDIV EDBGRDIV EDCTRDIV NUMFIRDIV NUMINDDIV INTEXPDIV CERTDIV MEMSDIV, lag(2 2) collapse) iv(Firm_age y*, eq(level)) nodiffsargan robust orthogonal small
Dynamic panel-data estimation, one-step system GMM
------------------------------------------------------------------------------
Group variable: FCode1 Number of obs = 697
Time variable : Year Number of groups = 192
Number of instruments = 30 Obs per group: min = 1
F(18, 191) = 2.99 avg = 3.63
Prob > F = 0.000 max = 4
------------------------------------------------------------------------------
| Robust
Adj_TobinQt1 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Adj_TobinQt1 |
L1. | .6119378 .1582645 3.87 0.000 .299767 .9241086
|
Firm_age | .0001865 .0007226 0.26 0.797 -.0012388 .0016118
FirmSize | -.0554582 .0435507 -1.27 0.204 -.1413602 .0304439
TEAMSIZE | .194183 .2038116 0.95 0.342 -.2078277 .5961936
GENDIV | -.1115258 .2900493 -0.38 0.701 -.683637 .4605855
EDLEVDIV | -.1567298 .3364569 -0.47 0.642 -.8203783 .5069187
EDBGRDIV | .1727845 .2767815 0.62 0.533 -.3731565 .7187255
EDCTRDIV | -.0414589 .1951742 -0.21 0.832 -.4264325 .3435148
NUMFIRDIV | .5470325 .3048599 1.79 0.074 -.054292 1.148357
NUMINDDIV | -.8538604 .3935092 -2.17 0.031 -1.630042 -.0776785
INTEXPDIV | -.1214908 .1830203 -0.66 0.508 -.4824914 .2395097
CERTDIV | -.0172855 .0992637 -0.17 0.862 -.2130794 .1785085
MEMSDIV | -.0050279 .0356161 -0.14 0.888 -.0752793 .0652235
y1 | 0 (omitted)
y2 | .0050771 .0340706 0.15 0.882 -.0621259 .07228
y3 | .0402348 .0292479 1.38 0.171 -.0174557 .0979252
y4 | .0370028 .0247063 1.50 0.136 -.0117293 .085735
y5 | 0 (omitted)
_cons | .6623994 .7719175 0.86 0.392 -.8601786 2.184977
------------------------------------------------------------------------------
Instruments for orthogonal deviations equation
GMM-type (missing=0, separate instruments for each period unless collapsed)
L2.(FirmSize TEAMSIZE GENDIV EDLEVDIV EDBGRDIV EDCTRDIV NUMFIRDIV
NUMINDDIV INTEXPDIV CERTDIV MEMSDIV) collapsed
L(3/4).Adj_TobinQt1 collapsed
Instruments for levels equation
Standard
Firm_age y1 y2 y3 y4 y5
_cons
GMM-type (missing=0, separate instruments for each period unless collapsed)
DL.(FirmSize TEAMSIZE GENDIV EDLEVDIV EDBGRDIV EDCTRDIV NUMFIRDIV
NUMINDDIV INTEXPDIV CERTDIV MEMSDIV) collapsed
DL2.Adj_TobinQt1 collapsed
------------------------------------------------------------------------------
Arellano-Bond test for AR(1) in first differences: z = -3.37 Pr > z = 0.001
Arellano-Bond test for AR(2) in first differences: z = 0.42 Pr > z = 0.671
------------------------------------------------------------------------------
Sargan test of overid. restrictions: chi2(11) = 18.88 Prob > chi2 = 0.063
(Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(11) = 14.19 Prob > chi2 = 0.223
(Robust, but weakened by many instruments.)
Am I doing it right?
In the case of doing GMM with interaction and non-linear, is there any difference with OLS? (I mean in adding interaction and non-linear variables to models).
Thanks for your read and help.
I am using GMM for my thesis. I have read papers and topics here but I am not sure about my knowledge so I would like to hear from you.
My data has 1 dependent, 8 independent and 4 control variables. I am coding as following:
xtabond2 Adj_TobinQt1 l.Adj_TobinQt1 Firm_age FirmSize TEAMSIZE GENDIV EDLEVDIV EDBGRDIV EDCTRDIV NUMFIRDIV NUMINDDIV INTEXPDIV CERTDIV MEMSDIV y*, gmm( Adj_TobinQt1 , lag (3 5) collapse) gmm( FirmSize TEAMSIZE GENDIV EDLEVDIV EDBGRDIV EDCTRDIV NUMFIRDIV NUMINDDIV INTEXPDIV CERTDIV MEMSDIV, lag(2 2) collapse) iv(Firm_age y*, eq(level)) nodiffsargan robust orthogonal small
Dynamic panel-data estimation, one-step system GMM
------------------------------------------------------------------------------
Group variable: FCode1 Number of obs = 697
Time variable : Year Number of groups = 192
Number of instruments = 30 Obs per group: min = 1
F(18, 191) = 2.99 avg = 3.63
Prob > F = 0.000 max = 4
------------------------------------------------------------------------------
| Robust
Adj_TobinQt1 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Adj_TobinQt1 |
L1. | .6119378 .1582645 3.87 0.000 .299767 .9241086
|
Firm_age | .0001865 .0007226 0.26 0.797 -.0012388 .0016118
FirmSize | -.0554582 .0435507 -1.27 0.204 -.1413602 .0304439
TEAMSIZE | .194183 .2038116 0.95 0.342 -.2078277 .5961936
GENDIV | -.1115258 .2900493 -0.38 0.701 -.683637 .4605855
EDLEVDIV | -.1567298 .3364569 -0.47 0.642 -.8203783 .5069187
EDBGRDIV | .1727845 .2767815 0.62 0.533 -.3731565 .7187255
EDCTRDIV | -.0414589 .1951742 -0.21 0.832 -.4264325 .3435148
NUMFIRDIV | .5470325 .3048599 1.79 0.074 -.054292 1.148357
NUMINDDIV | -.8538604 .3935092 -2.17 0.031 -1.630042 -.0776785
INTEXPDIV | -.1214908 .1830203 -0.66 0.508 -.4824914 .2395097
CERTDIV | -.0172855 .0992637 -0.17 0.862 -.2130794 .1785085
MEMSDIV | -.0050279 .0356161 -0.14 0.888 -.0752793 .0652235
y1 | 0 (omitted)
y2 | .0050771 .0340706 0.15 0.882 -.0621259 .07228
y3 | .0402348 .0292479 1.38 0.171 -.0174557 .0979252
y4 | .0370028 .0247063 1.50 0.136 -.0117293 .085735
y5 | 0 (omitted)
_cons | .6623994 .7719175 0.86 0.392 -.8601786 2.184977
------------------------------------------------------------------------------
Instruments for orthogonal deviations equation
GMM-type (missing=0, separate instruments for each period unless collapsed)
L2.(FirmSize TEAMSIZE GENDIV EDLEVDIV EDBGRDIV EDCTRDIV NUMFIRDIV
NUMINDDIV INTEXPDIV CERTDIV MEMSDIV) collapsed
L(3/4).Adj_TobinQt1 collapsed
Instruments for levels equation
Standard
Firm_age y1 y2 y3 y4 y5
_cons
GMM-type (missing=0, separate instruments for each period unless collapsed)
DL.(FirmSize TEAMSIZE GENDIV EDLEVDIV EDBGRDIV EDCTRDIV NUMFIRDIV
NUMINDDIV INTEXPDIV CERTDIV MEMSDIV) collapsed
DL2.Adj_TobinQt1 collapsed
------------------------------------------------------------------------------
Arellano-Bond test for AR(1) in first differences: z = -3.37 Pr > z = 0.001
Arellano-Bond test for AR(2) in first differences: z = 0.42 Pr > z = 0.671
------------------------------------------------------------------------------
Sargan test of overid. restrictions: chi2(11) = 18.88 Prob > chi2 = 0.063
(Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(11) = 14.19 Prob > chi2 = 0.223
(Robust, but weakened by many instruments.)
Am I doing it right?
In the case of doing GMM with interaction and non-linear, is there any difference with OLS? (I mean in adding interaction and non-linear variables to models).
Thanks for your read and help.
