[Nur Batrisya]

Thanks Professor. I ran the serial correlation test for post #13 and this came out. Is this a concern since we usually reject at AR(1)

 estat serial, ar(1/2)
Arellano-Bond test for autocorrelation of the first-differenced residuals
H0: no autocorrelation of order 1:     z =   -0.6024   Prob > |z|  =    0.5469
H0: no autocorrelation of order 2:     z =   -1.2017   Prob > |z|  =    0.2295

[Sebastian Kripfganz]

Nur Batrisya
I have amended my previous post after you answered. Sorry about that.

[Nur Batrisya]

No worries, Professor. Here are the new results. Seems like this won't work as it does not satisfy either test.

xtdpdgmm L(0/1).TQ WOB BS FS FA, model(fod) collapse gmm(TQ, lag(1 3)) gmm(WOB BS FS FA, lag(1 3)) two vce(r)

Generalized method of moments estimation

Fitting full model:
Step 1         f(b) =  1.1514968
Step 2         f(b) =  .36312104

Group variable: COMPANY                      Number of obs         =       897
Time variable: YEAR                          Number of groups      =        69

Moment conditions:     linear =      16      Obs per group:    min =        13
                    nonlinear =       0                        avg =        13
                        total =      16                        max =        13

                               (Std. Err. adjusted for 69 clusters in COMPANY)
------------------------------------------------------------------------------
             |              WC-Robust
          TQ |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          TQ |
         L1. |   .0796538   .1361854     0.58   0.559    -.1872647    .3465723
             |
         WOB |   .9326466   1.764236     0.53   0.597    -2.525193    4.390486
          BS |  -.3378666   .1514236    -2.23   0.026    -.6346513   -.0410818
          FS |   1.105903   .4678388     2.36   0.018     .1889554     2.02285
          FA |   .3497277    .861239     0.41   0.685     -1.33827    2.037725
       _cons |    -4.8908   2.503565    -1.95   0.051    -9.797697    .0160959
------------------------------------------------------------------------------
Instruments corresponding to the linear moment conditions:
 1, model(fodev):
   L1.TQ L2.TQ L3.TQ
 2, model(fodev):
   L1.WOB L2.WOB L3.WOB L1.BS L2.BS L3.BS L1.FS L2.FS L3.FS L1.FA L2.FA L3.FA
 3, model(level):
   _cons

. estat overid

Sargan-Hansen test of the overidentifying restrictions
H0: overidentifying restrictions are valid

2-step moment functions, 2-step weighting matrix       chi2(10)    =   25.0554
                                                       Prob > chi2 =    0.0052

2-step moment functions, 3-step weighting matrix       chi2(10)    =   24.5326
                                                       Prob > chi2 =    0.0063

. estat serial

Arellano-Bond test for autocorrelation of the first-differenced residuals
H0: no autocorrelation of order 1:     z =   -0.9951   Prob > |z|  =    0.3197
H0: no autocorrelation of order 2:     z =   -1.3727   Prob > |z|  =    0.1698

[Sebastian Kripfganz]

I am sorry; this is as much as I can help.

[Nur Batrisya]

It's okay, really appreciate it

[Christian Pasanje]

Dear Sebastian,
I also want to run a system gmm
My data has
N: 254
T: 17

Dependent variable: Return on Asset (ROA) and Return on Equity (ROE) (the plan is to run a gmm estimation of each Independently)

The independent variable: financial inclusion (FIN).

control variables : Capital adequacy ratio(Car), Cost income Ratio(CIR), Liquidity ratio(LR), Concentration Ratio(CR), Policy Rate(PR), Annual GDP growth rate(GDPR) and inflation Rate(INFL).

Can you help me with a code to run given my variables?

[Sebastian Kripfganz]

The following presentation has many examples for different situations:

• Kripfganz, S. (2019). Generalized method of moments estimation of linear dynamic panel data models. Proceedings of the 2019 London Stata Conference.