Dear all,

I am pretty new to Stata and just beginning my research journey.

I am trying to estimate the effect of bank specific variables like Size (Ln Assets), Risk Leverage, Gross NPA, Provision Coverage Ratio, NIM, non-Interest Income, Contingent Liabilities and Operating Efficiency on Stress level of banks. I have included GDP growth Rate, G-sec Yield, Call Money Rate, Stability, Inflation and USD-INR Exchange Rate as my macroeconomic control variables. The period of my study is from 2005 to 2022 and my sample consists of 39 banks.

In the analysis, I am trying to execute the Mundlak approach to decide between FE and RE (Ref: https://blog.stata.com/2015/10/29/fi...dlak-approach/). However, what I got in my results is that the constraints are getting dropped, although I am getting the chi value, (which supports the inference drawn from Hausman test, i.e. appropriateness of RE model). The commands entered are as follows (all variables are set as -xtset-).

Code:
. bysort LN_ASSETS: egen mean_LN_ASSETS = mean( LN_ASSETS)
(21 missing values generated)

. bysort RISK_LEV: egen mean_RISK_LEV=mean( RISK_LEV)
(21 missing values generated)

. bysort GNPA : egen mean_GNPA = mean( GNPA )
(21 missing values generated)

. bysort PCR : egen mean_PCR = mean( PCR )
(21 missing values generated)

. bysort NIM : egen mean_NIM = mean( NIM )
(21 missing values generated)

. bysort NONINT_INC : egen mean_NONINT_INC = mean( NONINT_INC )
(21 missing values generated)

. bysort CONT_LIAB : egen mean_CONT_LIAB = mean( CONT_LIAB )
(21 missing values generated)

. bysort OP_EFF : egen mean_OP_EFF = mean( OP_EFF )
(21 missing values generated)

. bysort GDP_GR : egen mean_GDP_GR = mean( GDP_GR )

. bysort GSEC_YLD : egen mean_GSEC_YLD = mean( GSEC_YLD )

.  bysort WTAVG_CMR : egen mean_WTAVG_CMR = mean( WTAVG_CMR )

.  bysort STABILITY : egen mean_STABILITY = mean( STABILITY )

.  bysort CP_INFL : egen mean_CP_INFL = mean( CP_INFL )

.  bysort USDINR_EXC : egen mean_USDINR_EXC = mean( USDINR_EXC )

Code:
. quietly xtreg STRESS_SCORE LN_ASSETS RISK_LEV GNPA PCR NIM NONINT_INC CONT_LIAB OP_EFF GDP_GR WTAVG_CMR STABILITY CP
> _INFL USDINR_EXC mean_LN_ASSETS mean_RISK_LEV mean_GNPA mean_PCR mean_NIM mean_NONINT_INC mean_CONT_LIAB mean_OP_EFF
>  mean_GDP_GR mean_GSEC_YLD mean_WTAVG_CMR mean_STABILITY mean_CP_INFL mean_USDINR_EXC, re vce(robust)
I have used the robust(vce) command as used in the example.

Code:
. estimates store mundlak

. test mean_LN_ASSETS mean_RISK_LEV mean_GNPA mean_PCR mean_NIM mean_NONINT_INC mean_CONT_LIAB mean_OP_EFF mean_GDP_GR
>  mean_GSEC_YLD mean_WTAVG_CMR mean_STABILITY mean_CP_INFL mean_USDINR_EXC

 ( 1)  o.mean_LN_ASSETS = 0
 ( 2)  o.mean_RISK_LEV = 0
 ( 3)  o.mean_GNPA = 0
 ( 4)  o.mean_PCR = 0
 ( 5)  o.mean_NIM = 0
 ( 6)  o.mean_NONINT_INC = 0
 ( 7)  o.mean_CONT_LIAB = 0
 ( 8)  o.mean_OP_EFF = 0
 ( 9)  o.mean_GDP_GR = 0
 (10)  mean_GSEC_YLD = 0
 (11)  o.mean_WTAVG_CMR = 0
 (12)  o.mean_STABILITY = 0
 (13)  o.mean_CP_INFL = 0
 (14)  o.mean_USDINR_EXC = 0
       Constraint 1 dropped
       Constraint 2 dropped
       Constraint 3 dropped
       Constraint 4 dropped
       Constraint 5 dropped
       Constraint 6 dropped
       Constraint 7 dropped
       Constraint 8 dropped
       Constraint 9 dropped
       Constraint 11 dropped
       Constraint 12 dropped
       Constraint 13 dropped
       Constraint 14 dropped

           chi2(  1) =    0.39
         Prob > chi2 =    0.5309
My questions are as follows.

i) Whether the approach is correct and the inference thus obtained is reliable? Or are there some issues ? If so, kindly suggest the correct way to proceed. What exactly should we interpret by the expression "constraint dropped"?

ii) The macroeconomic control variables, I'm considering in my model, don't change across banks (cross-sections), but are time-variant. In that case, would it be appropriate to consider them in the model. (I know it is pretty basic, but, I'm not sure whether such control variables would make any difference to the model outcomes, as they don't change between banks.)

I would really appreciate any help and suggestions for improvement.

Thanks and regards
pankaj