I'm using panel data and tobit regression to examine whether there would be a better merger efficiency gains if Small Banks merge with Small, Medium, Large, Government owned or Foreign banks.
I’m using -xttobit- and the coefficient of the independent variables differs when I switch the position of the dummy variables.
I am using STATA 14.2 for Windows.
Here is the code I have used
Code:
xtset n_merger Year
xttobit OverallEfficiency Ssize Msize Lsize Gov, ul(1) ll(0)
Obtaining starting values for full model:
Iteration 0: log likelihood = 328.33674
Iteration 1: log likelihood = 328.64894
Iteration 2: log likelihood = 328.65025
Fitting full model:
Iteration 0: log likelihood = 172.02173
Iteration 1: log likelihood = 181.43146
Iteration 2: log likelihood = 182.33997
Iteration 3: log likelihood = 182.42299
Iteration 4: log likelihood = 182.42312
Iteration 5: log likelihood = 182.42312
Random-effects tobit regression Number of obs = 401
Group variable: n_merger Number of groups = 99
Random effects u_i ~ Gaussian Obs per group:
min = 1
avg = 4.1
max = 6
Integration method: mvaghermite Integration pts. = 12
Wald chi2(4) = 200.24
Log likelihood = 182.42312 Prob > chi2 = 0.0000
-----------------------------------------------------------------------------------
OverallEfficiency | Coef. Std. Err. z P>|z| [95% Conf. Interval]
------------------+----------------------------------------------------------------
Ssize | -.2414938 .029473 -8.19 0.000 -.2992598 -.1837279
Msize | -.2081237 .0214828 -9.69 0.000 -.2502293 -.1660181
Lsize | -.2931646 .0234581 -12.50 0.000 -.3391416 -.2471876
Gov | .0124348 .0373215 0.33 0.739 -.0607141 .0855836
_cons | 1.057655 .0192292 55.00 0.000 1.019966 1.095343
------------------+----------------------------------------------------------------
/sigma_u | .0381107 .0087667 4.35 0.000 .0209282 .0552932
/sigma_e | .1076226 .0049301 21.83 0.000 .0979597 .1172855
------------------+----------------------------------------------------------------
rho | .1114244 .0489331 .0423219 .2377307
-----------------------------------------------------------------------------------
0 left-censored observations
316 uncensored observations
85 right-censored observations
.
And when I used
xttobit OverallEfficiency For Msize Lsize Gov, ul(1) ll(0)
Obtaining starting values for full model:
Iteration 0: log likelihood = 328.33674
Iteration 1: log likelihood = 328.64894
Iteration 2: log likelihood = 328.65025
Fitting full model:
Iteration 0: log likelihood = 172.02173
Iteration 1: log likelihood = 181.43146
Iteration 2: log likelihood = 182.33997
Iteration 3: log likelihood = 182.42299
Iteration 4: log likelihood = 182.42312
Iteration 5: log likelihood = 182.42312
Random-effects tobit regression Number of obs = 401
Group variable: n_merger Number of groups = 99
Random effects u_i ~ Gaussian Obs per group:
min = 1
avg = 4.1
max = 6
Integration method: mvaghermite Integration pts. = 12
Wald chi2(4) = 200.24
Log likelihood = 182.42312 Prob > chi2 = 0.0000
-----------------------------------------------------------------------------------
OverallEfficiency | Coef. Std. Err. z P>|z| [95% Conf. Interval]
------------------+----------------------------------------------------------------
For | .2414938 .029473 8.19 0.000 .1837279 .2992598
Msize | .0333702 .0237798 1.40 0.161 -.0132375 .0799778
Lsize | -.0516707 .0261262 -1.98 0.048 -.1028771 -.0004644
Gov | .2539286 .0392377 6.47 0.000 .1770241 .3308331
_cons | .8161609 .0224829 36.30 0.000 .7720953 .8602266
------------------+----------------------------------------------------------------
/sigma_u | .0381107 .0087667 4.35 0.000 .0209282 .0552932
/sigma_e | .1076226 .0049301 21.83 0.000 .0979597 .1172855
------------------+----------------------------------------------------------------
rho | .1114244 .0489331 .0423219 .2377307
-----------------------------------------------------------------------------------
0 left-censored observations
316 uncensored observations
85 right-censored observations
.Code:
* Example generated by -dataex-. To install: ssc install dataex clear input long n_merger int Year double OverallEfficiency byte(Ssize Msize Lsize Gov For) 1 2013 .777840162468523 1 0 0 0 0 1 2014 .667572052659211 0 1 0 0 0 1 2015 .768242260625778 0 1 0 0 0 1 2016 .567118419945576 1 0 0 0 0 1 2017 .859045334409472 1 0 0 0 0 1 2018 .926879924205817 0 1 0 0 0 2 2013 .636795487468595 0 0 1 0 0 2 2016 .679302254717641 0 0 1 0 0 2 2017 .883812355073254 0 0 1 0 0 3 2013 .807119089060473 0 1 0 0 0 3 2016 .612385336082278 0 1 0 0 0 3 2017 .803911063742677 0 1 0 0 0 4 2013 .776070621706571 0 1 0 0 0 4 2014 .701530532508148 0 1 0 0 0 4 2015 .731823498361102 0 1 0 0 0 4 2016 .671369058466319 0 1 0 0 0 4 2017 .965833027226234 0 1 0 0 0 5 2013 .523401324552278 0 0 1 0 0 5 2016 .623156844015406 0 0 1 0 0 5 2017 .818176523074846 0 0 1 0 0 6 2013 1.01338399319672 0 0 0 1 0 6 2016 .993182067949074 0 0 0 1 0 6 2017 1.0078169674446 0 0 0 1 0 7 2013 1.18448548991908 0 1 0 0 0 7 2016 .66830374684709 0 1 0 0 0 7 2017 .792138182852533 0 1 0 0 0 8 2013 .999523000545116 0 0 0 0 1 8 2016 .992094633344752 0 0 0 0 1 8 2017 1.00046492807004 0 0 0 0 1 9 2013 .756968629268395 0 0 1 0 0 9 2016 .69458727510718 0 0 1 0 0 9 2017 .862915656220076 0 0 1 0 0 10 2013 .801900948472236 1 0 0 0 0 10 2014 .685078263065857 0 1 0 0 0 10 2015 .716068248948041 0 1 0 0 0 10 2016 .559047609212464 1 0 0 0 0 10 2017 .841991507330655 1 0 0 0 0 10 2018 .949790565653028 0 1 0 0 0 11 2014 .680172473695335 0 1 0 0 0 11 2015 .75541158131174 0 1 0 0 0 11 2016 .566548333784565 1 0 0 0 0 11 2017 .786254866737269 1 0 0 0 0 11 2018 .900417035248462 0 1 0 0 0 12 2013 1.00498847869289 0 0 0 0 1 12 2016 1.00328749848233 0 0 0 0 1 12 2017 1.00589596338716 0 0 0 0 1 13 2013 1.00921479915483 0 0 0 0 1 13 2016 .991135988477932 0 0 0 0 1 13 2017 1.01109899316294 0 0 0 0 1 14 2013 .852176575054603 0 1 0 0 0 14 2016 .657874157276093 0 1 0 0 0 14 2017 .85646922850085 0 1 0 0 0 15 2013 .750996292168454 0 1 0 0 0 15 2016 .813548591097452 0 1 0 0 0 15 2017 .988408162634215 0 1 0 0 0 16 2013 .747785235898137 0 1 0 0 0 16 2016 .663220353007534 0 1 0 0 0 16 2017 .936122234710201 0 1 0 0 0 17 2013 .661188619179225 0 1 0 0 0 17 2016 .608865662202188 0 0 1 0 0 17 2017 .77149317636079 0 0 1 0 0 18 2013 .824611513115011 0 0 1 0 0 18 2016 .960324172316378 0 1 0 0 0 18 2017 .975401108784843 0 1 0 0 0 19 2013 .963429261285566 0 1 0 0 0 19 2016 .669946323029468 0 1 0 0 0 19 2017 .981601192052231 0 1 0 0 0 19 2018 .916651291903188 0 1 0 0 0 20 2013 .617431302902067 0 0 1 0 0 20 2014 .865348404880229 0 0 1 0 0 20 2015 .652834671144123 0 0 1 0 0 20 2016 .746964385643763 0 0 1 0 0 20 2017 .897693217361013 0 0 1 0 0 20 2018 .907410006573264 0 0 1 0 0 21 2013 .821333099939362 0 1 0 0 0 21 2014 .77980031057586 0 1 0 0 0 21 2015 .685685648519337 0 1 0 0 0 21 2016 .815044695863181 0 1 0 0 0 21 2017 .822747634428948 0 1 0 0 0 21 2018 .878671094308265 0 1 0 0 0 22 2013 .823898797125514 0 1 0 0 0 22 2014 .762768880702798 1 0 0 0 0 22 2015 .807446721174141 1 0 0 0 0 22 2016 .925791790558292 0 1 0 0 0 22 2017 1.09338805847993 0 1 0 0 0 22 2018 .836051418235281 0 1 0 0 0 23 2013 1.01023987403303 0 0 0 1 0 23 2014 1.01177052424583 0 0 0 1 0 23 2015 .997455597264312 0 0 0 1 0 23 2016 1.00808856535401 0 0 0 1 0 23 2017 1.0017251902628 0 0 0 1 0 23 2018 1.00128354833426 0 0 0 1 0 24 2013 1.1953595253649 0 1 0 0 0 24 2014 1.15097799734811 0 1 0 0 0 24 2015 .800019319800766 0 1 0 0 0 24 2016 .850393681631707 0 1 0 0 0 24 2017 .809949278887908 0 1 0 0 0 24 2018 .79576330040545 0 0 1 0 0 25 2013 .998861294517819 0 0 0 0 1 25 2014 1.00322916778966 0 0 0 0 1 25 2015 1.01742782147918 0 0 0 0 1 25 2016 1.01443545465809 0 0 0 0 1 25 2017 .999163528767699 0 0 0 0 1 25 2018 1.0005439715517 0 0 0 0 1 26 2013 .74599112388065 0 0 1 0 0 26 2014 .972970532939351 0 0 1 0 0 26 2015 .856772579862182 0 0 1 0 0 26 2016 .755858231589364 0 0 1 0 0 26 2017 .874836825935082 0 0 1 0 0 26 2018 .804954898326352 0 0 1 0 0 27 2013 .872377031289671 1 0 0 0 0 27 2014 .828775616984605 1 0 0 0 0 27 2015 .821585302400639 1 0 0 0 0 27 2016 .843467317012207 1 0 0 0 0 27 2017 1.12746114546205 1 0 0 0 0 27 2018 .990589835281587 1 0 0 0 0 28 2014 .868210895148766 1 0 0 0 0 28 2015 .90256083335277 1 0 0 0 0 28 2016 .862855792661279 1 0 0 0 0 28 2017 .83535845507259 1 0 0 0 0 28 2018 .890716842684073 1 0 0 0 0 29 2013 1.00365765400966 0 0 0 0 1 29 2014 1.00527069737532 0 0 0 0 1 29 2015 .68452074422836 0 0 0 0 1 29 2016 1.01027988856022 0 0 0 0 1 29 2017 1.00480849903707 0 0 0 0 1 29 2018 1.00246903696495 0 0 0 0 1 30 2013 1.00820626082979 0 0 0 0 1 30 2014 1.0053051023066 0 0 0 0 1 30 2015 .994141019029613 0 0 0 0 1 30 2016 1.00594083430092 0 0 0 0 1 30 2017 1.0077989357137 0 0 0 0 1 30 2018 1.00253904258351 0 0 0 0 1 31 2013 .874259507618924 0 1 0 0 0 31 2014 .741145746076467 0 1 0 0 0 31 2015 .915046111219228 0 1 0 0 0 31 2016 .869798538660265 0 1 0 0 0 31 2017 .895002338464555 0 1 0 0 0 31 2018 .897783918070956 0 1 0 0 0 32 2013 .745900428963911 0 1 0 0 0 32 2014 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1.01198878208944 0 0 0 1 0 45 2015 .995646173930971 0 0 0 1 0 46 2014 1.07628685532869 0 1 0 0 0 46 2015 .785339139732285 0 1 0 0 0 47 2014 1.00313696930831 0 0 0 0 1 47 2015 1.04190004953048 0 0 0 0 1 48 2014 .949524078705721 0 0 1 0 0 48 2015 .835180775700745 0 0 1 0 0 49 2013 .790239627777075 0 1 0 0 0 49 2014 .766589560038174 1 0 0 0 0 49 2015 .758384896241101 1 0 0 0 0 49 2016 .990826333211286 0 1 0 0 0 49 2017 1.12484032050099 0 1 0 0 0 49 2018 .85019880437723 0 1 0 0 0 50 2014 .773424179981518 1 0 0 0 0 50 2015 .794488616115139 1 0 0 0 0 50 2016 .872376034164794 0 1 0 0 0 50 2017 .90088286355704 0 1 0 0 0 50 2018 .822654570288226 0 1 0 0 0 51 2014 1.00636817199934 0 0 0 0 1 51 2015 .685431717659472 0 0 0 0 1 52 2014 1.00357151012239 0 0 0 0 1 52 2015 .993967235831946 0 0 0 0 1 53 2014 .756706938197038 0 1 0 0 0 53 2015 .850270943649216 0 1 0 0 0 54 2014 .795997585364037 0 1 0 0 0 54 2015 .700540139560121 0 1 0 0 0 55 2014 .783485803567604 0 1 0 0 0 55 2015 .717725945061923 0 1 0 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Thank you in Advance
Haben Mehari
0 Response to Confusion about -xttobit-
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