Dear all,

I am new to the xtabond2 command and I have been struggling with this for a while so I really appreciate any assistance and help in this matter.

My panel data consists of 28 observations (countries), 16 years, dependent variable (log_depo_ratio) and 14 explanatory variables - L.depo_ratio, ir, log_hh_debt, L.log_income, log_m2, log_convergence, log_wealth, log_tot, lab_product, govsaving_ratio, log_inflindex, log_old, log_urb_rate, log_unrate, out of which only the last three variables are exogenous while all the rest are endogenous. Namely, I am trying to analyse the determinants of deposit rates.

This is the code I used:

Code:
xtabond2 log_depo_ratio L.log_depo_ratio ir log_hh_debt L.log_income log_m2 log_convergence log_wealth log_tot lab_product govsaving_ratio log_inflindex log_old log_urb_rate log_unrate, gmm(L.log_depo_ratio ir log_hh_debt L.log_income log_m2 log_convergence log_wealth log_tot lab_product govsaving_ratio log_inflindex, laglimits(0 0) eq(level) collapse) gmm(L.log_depo_ratio ir log_hh_debt L.log_income log_m2 log_convergence log_wealth log_tot lab_product govsaving_ratio log_inflindex, laglimits(1 1) eq(diff) collapse) iv( log_old log_urb_rate log_unrate, eq(level)) robust nodiffsargan
And this is the output that I got:

Code:
 xtabond2 log_depo_ratio L.log_depo_ratio ir log_hh_debt L.log_income log_m2 log_convergence log_wealth log_tot lab_product govsaving_ratio log_inflindex log_old log_urb_rate 
> log_unrate, gmm(L.log_depo_ratio ir log_hh_debt L.log_income log_m2 log_convergence log_wealth log_tot lab_product govsaving_ratio log_inflindex, laglimits(0 0) eq(level) col
> lapse) gmm(L.log_depo_ratio ir log_hh_debt L.log_income log_m2 log_convergence log_wealth log_tot lab_product govsaving_ratio log_inflindex, laglimits(1 1) eq(diff) collapse)
>  iv( log_old log_urb_rate log_unrate, eq(level)) robust nodiffsargan
Favoring speed over space. To switch, type or click on mata: mata set matafavor space, perm.

Dynamic panel-data estimation, one-step system GMM
------------------------------------------------------------------------------
Group variable: ctry_dum                        Number of obs      =       420
Time variable : year                            Number of groups   =        28
Number of instruments = 26                      Obs per group: min =        15
Wald chi2(14) =   1826.52                                      avg =     15.00
Prob > chi2   =     0.000                                      max =        15
---------------------------------------------------------------------------------
                |               Robust
 log_depo_ratio |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
 log_depo_ratio |
            L1. |   .6646304   .0640918    10.37   0.000     .5390128     .790248
                |
             ir |   .0021721   .0050198     0.43   0.665    -.0076666    .0120108
    log_hh_debt |   .1005615   .0366328     2.75   0.006     .0287625    .1723605
                |
     log_income |
            L1. |   .3276255   .1364211     2.40   0.016     .0602451    .5950059
                |
         log_m2 |   .0005251    .021366     0.02   0.980    -.0413516    .0424017
log_convergence |  -.7696703   .2441639    -3.15   0.002    -1.248223   -.2911179
     log_wealth |   .1544913   .0517407     2.99   0.003     .0530813    .2559012
        log_tot |  -.0583764   .3766962    -0.15   0.877    -.7966874    .6799347
    lab_product |  -.0069043   .0026771    -2.58   0.010    -.0121514   -.0016573
govsaving_ratio |   .0001679    .002976     0.06   0.955    -.0056648    .0060007
  log_inflindex |  -.2735538   .1781045    -1.54   0.125    -.6226323    .0755246
        log_old |  -.2359759   .0927227    -2.54   0.011    -.4177089   -.0542428
   log_urb_rate |  -.0201112   .1189348    -0.17   0.866    -.2532192    .2129967
     log_unrate |   .0458677   .0315098     1.46   0.145    -.0158904    .1076259
          _cons |   2.579339   2.135743     1.21   0.227     -1.60664    6.765318
---------------------------------------------------------------------------------
Instruments for first differences equation
  GMM-type (missing=0, separate instruments for each period unless collapsed)
    L.(L.log_depo_ratio ir log_hh_debt L.log_income log_m2 log_convergence
    log_wealth log_tot lab_product govsaving_ratio log_inflindex) collapsed
Instruments for levels equation
  Standard
    log_old log_urb_rate log_unrate
    _cons
  GMM-type (missing=0, separate instruments for each period unless collapsed)
    D.(L.log_depo_ratio ir log_hh_debt L.log_income log_m2 log_convergence
    log_wealth log_tot lab_product govsaving_ratio log_inflindex) collapsed
------------------------------------------------------------------------------
Arellano-Bond test for AR(1) in first differences: z =  -3.49  Pr > z =  0.000
Arellano-Bond test for AR(2) in first differences: z =  -0.17  Pr > z =  0.863
------------------------------------------------------------------------------
Sargan test of overid. restrictions: chi2(11)   = 100.87  Prob > chi2 =  0.000
  (Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(11)   =  15.44  Prob > chi2 =  0.163
  (Robust, but weakened by many instruments.)


.
According to my research and what I read in other working papers and some topics on Statalist, both my Hansen and AR(1) are good. There was a discussion on shoulds we look at Hansen or Sargan, but as I understood we can just focus on one of the tests because they both show the validation of our instruments. Therefore, is my logic flawed if I just focus on AR(1) being 0.000 and Hansen being not too big?

Lastly, my biggest concern are the p-values. I tried a lot of things, but I can't get them at the conventional 5% significance level. I know that this may sound ridicoulous, but is there a statistical theory that might back up the idea of p-values not being so important?As I understood, the Fischer was not so strict about using them strictly at the alpha significance?

If not, what else could I do to make them smaller and acceptable?