I need your help to make sure that i did input the interaction term into xtabond2 properly using (##) or i should generate a variable that equals to (OBSR*EA) and include it separately. Also do i have to include the interaction term in the gmmstyle equation?
Code:
xtabond2 L(0/1).zs310 c.OBSR##c.EA_w Size AssetG RegP LLPAR NIasset LoanAsset LiqR DepFL GDPG T10Y3M y* if typeD1==1 , gmm(L.zs310 OBSR LiqR ,laglimits(1 2)) iv(GDPG RegP LoanAsset T10Y3M y*, eq(leve > l)) iv(GDPG T10Y3M y*, eq(diff)) twostep robust nodiffsargan Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm. Warning: Two-step estimated covariance matrix of moments is singular. Using a generalized inverse to calculate optimal weighting matrix for two-step estimation. Dynamic panel-data estimation, two-step system GMM ------------------------------------------------------------------------------ Group variable: id Number of obs = 2760 Time variable : year Number of groups = 333 Number of instruments = 82 Obs per group: min = 4 Wald chi2(15) = 1704.86 avg = 8.29 Prob > chi2 = 0.000 max = 9 ------------------------------------------------------------------------------- | Corrected zs310 | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- zs310 | L1. | .9425492 .0348098 27.08 0.000 .8743231 1.010775 | OBSR | .5598905 .2541442 2.20 0.028 .0617769 1.058004 EA_w | 1.330796 .579164 2.30 0.022 .1956559 2.465937 | c.OBSR#c.EA_w | -.0516348 .0261288 -1.98 0.048 -.1028463 -.0004233 | Size | 2.584476 1.024467 2.52 0.012 .5765586 4.592394 AssetG | -.1463115 .0429861 -3.40 0.001 -.2305627 -.0620603 RegP | -3.669736 3.700686 -0.99 0.321 -10.92295 3.583475 LLPAR | 5.596102 1.731068 3.23 0.001 2.203271 8.988932 NIasset | 2.239878 1.09118 2.05 0.040 .1012038 4.378552 LoanAsset | .0003182 .1030633 0.00 0.998 -.2016821 .2023185 LiqR | -.029229 .1119035 -0.26 0.794 -.2485558 .1900978 DepFL | .4377712 .1632081 2.68 0.007 .1178892 .7576531 GDPG | .3116449 .2350726 1.33 0.185 -.1490888 .7723787 T10Y3M | .601936 .3151627 1.91 0.056 -.0157716 1.219644 year | .0498398 .1343316 0.37 0.711 -.2134453 .3131248 _cons | -177.7768 269.1938 -0.66 0.509 -705.387 349.8333 ------------------------------------------------------------------------------- Instruments for first differences equation Standard D.(GDPG T10Y3M year) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/2).(L.zs310 OBSR LiqR) Instruments for levels equation Standard GDPG RegP LoanAsset T10Y3M year _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D.(L.zs310 OBSR LiqR) ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -4.21 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = -1.32 Pr > z = 0.188 ------------------------------------------------------------------------------ Sargan test of overid. restrictions: chi2(66) = 181.73 Prob > chi2 = 0.000 (Not robust, but not weakened by many instruments.) Hansen test of overid. restrictions: chi2(66) = 72.56 Prob > chi2 = 0.271 (Robust, but weakened by many instruments.)
Code:
xtabond2 L(0/1).zs310 c.OBSR##c.EA_w Size AssetG RegP LLPAR NIasset LoanAsset LiqR DepFL GDPG T10Y3M y* if typeD1==1 , gmm(L.zs310 OBSR LiqR ,laglimits(1 2)) iv(GDPG RegP LoanAsset T10Y3M y*, eq(leve > l)) twostep robust nodiffsargan Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm. Warning: Two-step estimated covariance matrix of moments is singular. Using a generalized inverse to calculate optimal weighting matrix for two-step estimation. Dynamic panel-data estimation, two-step system GMM ------------------------------------------------------------------------------ Group variable: id Number of obs = 2760 Time variable : year Number of groups = 333 Number of instruments = 79 Obs per group: min = 4 Wald chi2(15) = 1753.11 avg = 8.29 Prob > chi2 = 0.000 max = 9 ------------------------------------------------------------------------------- | Corrected zs310 | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- zs310 | L1. | .9572188 .0388439 24.64 0.000 .8810861 1.033352 | OBSR | .6133742 .2603226 2.36 0.018 .1031513 1.123597 EA_w | 1.342762 .6167183 2.18 0.029 .1340167 2.551508 | c.OBSR#c.EA_w | -.0550683 .0267391 -2.06 0.039 -.107476 -.0026607 | Size | 2.315932 1.0324 2.24 0.025 .2924644 4.339399 AssetG | -.1349076 .0396543 -3.40 0.001 -.2126286 -.0571866 RegP | -2.522424 3.659951 -0.69 0.491 -9.695797 4.650948 LLPAR | 4.638541 1.866803 2.48 0.013 .9796753 8.297408 NIasset | 2.004519 1.081241 1.85 0.064 -.1146744 4.123713 LoanAsset | .0135977 .1025165 0.13 0.894 -.1873309 .2145262 LiqR | -.0137577 .1131039 -0.12 0.903 -.2354371 .2079218 DepFL | .4468185 .1599196 2.79 0.005 .1333818 .7602552 GDPG | .2842781 .2387337 1.19 0.234 -.1836313 .7521875 T10Y3M | .4566393 .3484915 1.31 0.190 -.2263914 1.13967 year | .0268442 .1285312 0.21 0.835 -.2250722 .2787607 _cons | -131.8118 258.7221 -0.51 0.610 -638.8979 375.2743 ------------------------------------------------------------------------------- Instruments for first differences equation GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/2).(L.zs310 OBSR LiqR) Instruments for levels equation Standard GDPG RegP LoanAsset T10Y3M year _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D.(L.zs310 OBSR LiqR) ------------------------------------------------------------------------------ Arellano-Bond test for AR(1) in first differences: z = -4.16 Pr > z = 0.000 Arellano-Bond test for AR(2) in first differences: z = -1.39 Pr > z = 0.165 ------------------------------------------------------------------------------ Sargan test of overid. restrictions: chi2(63) = 178.40 Prob > chi2 = 0.000 (Not robust, but not weakened by many instruments.) Hansen test of overid. restrictions: chi2(63) = 68.31 Prob > chi2 = 0.302 (Robust, but weakened by many instruments.)
Best,
Sad
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