Diagnostic test of 3 stage least square --help

[cklint]

cry: I obtain following results by estimating system consisting of two equations. In this regards, I like to conduct some diagnostic test but Evews appears to provides only System Residual Portmanteau Tests for Autocorrelations.
My quesions are

(1) Does estimation output provides implications on whether my estimation is appropriate or not?
(2) Are there any diagnostic tests that I have to conduct?
(3) What does the results of following autocorrelation tests for this sytem? -- Except lag 1, no autocorrelation exists so my model is OK?

Thank you.

------------ autocorrelation test result --------------------

Null Hypothesis: no residual autocorrelations up to lag h
Date: 06/29/11 Time: 11:30
Sample: 1999Q1 2009Q3
Included observations: 43

Lags Q-Stat Prob. Adj Q-Stat Prob. df

1 10.82315 0.0286 11.08084 0.0257 4
2 11.89759 0.1558 12.20770 0.1422 8
3 16.19051 0.1827 16.82259 0.1564 12
4 18.38523 0.3018 19.24240 0.2563 16

------------ estimation output ---------------------------

System: COEFF_RATIO

Estimation Method: Three-Stage Least Squares
Date: 06/29/11 Time: 11:26
Sample: 1999Q1 2009Q3
Included observations: 43
Total system (balanced) observations 86
Linear estimation after one-step weighting matrix

Coefficient Std. Error t-Statistic Prob.

C(1) 0.005819 0.004812 1.209260 0.2306
C(2) -1.028896 0.043499 -23.65327 0.0000
C(4) 0.228810 0.164468 1.391217 0.1686
C(6) 0.036068 0.052071 0.692672 0.4908
C(8) -0.017446 0.054385 -0.320796 0.7493
C(10) -0.336310 0.432480 -0.777632 0.4394
C(12) -0.047116 0.149248 -0.315693 0.7532
C(14) -2.629151 1.346337 -1.952818 0.0548
C(16) 0.007033 0.004433 1.586515 0.1171
C(17) -0.820989 0.045268 -18.13606 0.0000
C(19) 0.250807 0.147813 1.696785 0.0942
C(21) -0.029336 0.048103 -0.609861 0.5439
C(23) 0.010782 0.042913 0.251240 0.8024
C(25) -0.359385 0.393304 -0.913758 0.3640
C(27) -0.011578 0.134043 -0.086375 0.9314
C(29) -0.350884 0.174005 -2.016523 0.0476

Determinant residual covariance 8.42E-09


Equation: DNDA/GDP_NO_SA = C(1) + C(2)*DNFA/GDP_NO_SA +C(4)*CA
/GDP_NO_SA +C(6)*DRUS_ADJ +C(8)*FXDIF +C(10)*INF +C(12)*YC
+C(14)*D1SDRKO
Instruments: DNFA/GDP_NO_SA CA/GDP_NO_SA DRUS_ADJ FXDIF INF
YC D1SDRKO C
Observations: 43

R-squared 0.972699 Mean dependent var -0.026997
Adjusted R-squared 0.967239 S.D. dependent var 0.083736
S.E. of regression 0.015156 Sum squared resid 0.008040
Durbin-Watson stat 2.753145

Equation: DNFA/GDP_NO_SA = C(16) + C(17)*DNDA/GDP_NO_SA + C(19)
*CA/GDP_NO_SA + C(21)*DRUS_ADJ + C(23)*FXDIF + C(25)*INF +
C(27)*YC+ C(29)*D2SDFX
Instruments: DNDA/GDP_NO_SA CA/GDP_NO_SA DRUS_ADJ FXDIF INF
YC D2SDFX C
Observations: 43
R-squared 0.973912 Mean dependent var 0.032511
Adjusted R-squared 0.968695 S.D. dependent var 0.077905
S.E. of regression 0.013784 Sum squared resid 0.006650
Durbin-Watson stat 2.859623