___ ____ ____ ____ ____ (R) /__ / ____/ / ____/ ___/ / /___/ / /___/ 13.1 Copyright 1985-2013 StataCorp LP Statistics/Data Analysis StataCorp 4905 Lakeway Drive Special Edition College Station, Texas 77845 USA 800-STATA-PC http://www.stata.com 979-696-4600 stata@stata.com 979-696-4601 (fax) Single-user Stata perpetual license: Serial number: 401306001168 Licensed to: Houston H. Stokes Econometric Software and Consulting Notes: 1. (/v# option or -set maxvar-) 5000 maximum variables 2. Stata running in batch mode . do jeh74.do . * Neuburger-Stokes JEH74 Paper on German Banking . infile using "c:\master\master1\class\e323\jeh74.dct", clear dictionary { * Data set built 27/ 1/15 at 20:25:41 by b34s double time double nnpi double mbcbca double rnnp _newline double mbcbsc double mbbssc double l double curr _newline double ibond double idisom double idisof double kdp _newline double nnp double mbcbdb double mbcbfb double lncdp0 _newline double lncdp1 double lncdp2 double agwf double nwunsk _newline double rnnpf double ragpf double wnsk double pknagr _newline double pecemp double wdp double lnkdl double lkdlsq _newline double rnnpi double lny double lnkdp double lnl _newline double mbcb double d_ca0 double cadmb double d_ca1 _newline double mbdy double mbdyp double dbdmb double kpc1 _newline double kc1 double lnmdp0 double lnmdp1 double lnmdp2 _newline double cadmb1 double cadmb2 double cadmb3 double cadmb4 _newline double gen_p double yp double mbdy1 double kp _newline double lpc1 double mbdy2 double lp double util _newline double tbdmb double lnqdl1 double lnkc1 double lnlc1 _newline double lnlc2 double lnlc3 double lnkpc1 double lnkp _newline double lnlp double mbdyp1 double lnlpc1 double mpdyp2 _newline double lnyp } (28 observations read) . summ Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- time | 28 17.5 8.225975 4 31 nnpi | 28 1.819643 .5587806 1.051 2.911 mbcbca | 28 24871.29 18837.28 5078.3 63795.2 rnnp | 28 37137.04 8804.961 24142 52440 mbcbsc | 28 653.9321 352.4786 100.27 1593.34 -------------+-------------------------------------------------------- mbbssc | 28 666.4393 361.1621 104.31 1552.67 l | 28 1.258705 .1484945 1.038164 1.532916 curr | 28 4333.071 1263.939 2835 6653 ibond | 28 3.640357 .1642149 3.35 4.09 idisom | 28 3.190357 .9172846 1.74 5.12 -------------+-------------------------------------------------------- idisof | 28 4.156429 .8079018 3.12 6.03 kdp | 28 1.703368 .4502473 1.0832 2.5521 nnp | 28 32780.86 10066.39 18935 52440 mbcbdb | 28 8691.421 4288.591 3707.5 17539.1 mbcbfb | 28 1999.679 1815.947 242 7523.1 -------------+-------------------------------------------------------- lncdp0 | 28 9.769193 .7417146 8.579095 10.86676 lncdp1 | 28 9.686426 .7440005 8.549292 10.79947 lncdp2 | 28 9.604805 .7427307 8.514066 10.77499 agwf | 28 9919.571 344.2007 9543 10701 nwunsk | 28 98.31476 7.247483 83.10018 114.1827 -------------+-------------------------------------------------------- rnnpf | 28 32468.39 8175.107 20548 48480 ragpf | 28 9258.071 1179.593 7267 11270 wnsk | 28 89.618 9.544249 72.27439 105.9681 pknagr | 28 .7254198 .0387419 .6618388 .7890007 pecemp | 28 .9758929 .0154401 .933 .998 -------------+-------------------------------------------------------- wdp | 28 .9335714 .053 .84 1.01 lnkdl | 28 .3399296 .1895905 .0556033 .6478863 lkdlsq | 28 .1502129 .1345788 .0030917 .4197566 rnnpi | 28 1.693449 .4015062 1.100875 2.391264 lny | 28 .4990491 .2414316 .0961055 .8718221 -------------+-------------------------------------------------------- lnkdp | 28 .498854 .2650977 .0799196 .9369165 lnl | 28 .2234309 .1172542 .0374533 .4271718 mbcb | 28 36216.32 24821.9 9999.63 87662.45 d_ca0 | 28 .0955238 .1490648 -.1402834 .567006 cadmb | 28 .6464127 .0752644 .5078488 .7594519 -------------+-------------------------------------------------------- d_ca1 | 28 .0943183 .1494182 -.1402834 .567006 mbdy | 28 .9904934 .396085 .5038492 1.671671 mbdyp | 28 17300.04 6986.719 8977.821 29120.94 dbdmb | 28 .27562 .0635159 .1739719 .4221556 kpc1 | 28 1.972416 .6265978 1.11605 3.154204 -------------+-------------------------------------------------------- kc1 | 28 1.737597 .4621464 1.092255 2.589435 lnmdp0 | 28 10.21232 .6317884 9.256667 11.18457 lnmdp1 | 28 10.14221 .6288162 9.221498 11.14083 lnmdp2 | 28 10.07368 .6204649 9.221498 11.11248 cadmb1 | 28 .6386572 .0777222 .5078488 .7594519 -------------+-------------------------------------------------------- cadmb2 | 28 .630869 .0810787 .4927309 .7594519 cadmb3 | 28 .6425349 .0739 .5092149 .7536496 cadmb4 | 28 .6386463 .0739286 .5037202 .7390728 gen_p | 28 1.058562 .077241 .954695 1.217348 yp | 28 1.877715 .5712538 1.053237 3.010289 -------------+-------------------------------------------------------- mbdy1 | 28 .9495414 .382078 .5038492 1.642605 kp | 28 1.933492 .61129 1.106829 3.108814 lpc1 | 28 1.347976 .1766366 1.055688 1.650123 mbdy2 | 28 .9111826 .3662614 .5038492 1.642605 lp | 28 1.481092 .2578146 1.072752 1.935499 -------------+-------------------------------------------------------- util | 28 .9120363 .039861 .8481168 .978968 tbdmb | 28 .3277672 .0601266 .2343944 .4463565 lnqdl1 | 28 .2948324 .1757193 -.002324 .6011862 lnkc1 | 28 .5182813 .2670114 .0882443 .9514396 lnlc1 | 28 .1364766 .0737143 .0222392 .2684772 -------------+-------------------------------------------------------- lnlc2 | 28 .4407997 .1708848 .1626164 .7148539 lnlc3 | 28 .3538453 .1278032 .1474024 .5561592 lnkpc1 | 28 .6301826 .320389 .1097957 1.148736 lnkp | 28 .6107836 .3185627 .1014993 1.134241 lnlp | 28 .3780277 .1754778 .0702268 .6603649 -------------+-------------------------------------------------------- mbdyp1 | 28 16596.76 6739.478 8977.821 28697.58 lnlpc1 | 28 .290253 .1319502 .0541924 .5008499 mpdyp2 | 28 15924.1 6430.318 8977.821 28697.58 lnyp | 28 .5850854 .306898 .0518684 1.102036 . tsset time time variable: time, 4 to 31 delta: 1 unit . * Kmenta test . regress lnyp lnkdl lkdlsq lnlpc1 Source | SS df MS Number of obs = 28 -------------+------------------------------ F( 3, 24) = 3228.09 Model | 2.53674485 3 .845581617 Prob > F = 0.0000 Residual | .006286673 24 .000261945 R-squared = 0.9975 -------------+------------------------------ Adj R-squared = 0.9972 Total | 2.54303152 27 .094186353 Root MSE = .01618 ------------------------------------------------------------------------------ lnyp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lnkdl | .5793272 .1315929 4.40 0.000 .3077328 .8509217 lkdlsq | .0370506 .1040697 0.36 0.725 -.1777386 .2518399 lnlpc1 | 1.462008 .1403393 10.42 0.000 1.172362 1.751654 _cons | -.0417628 .0124952 -3.34 0.003 -.0675516 -.015974 ------------------------------------------------------------------------------ . estat dwatson Durbin-Watson d-statistic( 4, 28) = .7470426 . prais lnyp lnkdl lkdlsq lnlpc1 Iteration 0: rho = 0.0000 Iteration 1: rho = 0.6083 Iteration 2: rho = 0.6843 Iteration 3: rho = 0.6903 Iteration 4: rho = 0.6907 Iteration 5: rho = 0.6908 Iteration 6: rho = 0.6908 Iteration 7: rho = 0.6908 Prais-Winsten AR(1) regression -- iterated estimates Source | SS df MS Number of obs = 28 -------------+------------------------------ F( 3, 24) = 536.97 Model | .242679719 3 .08089324 Prob > F = 0.0000 Residual | .003615536 24 .000150647 R-squared = 0.9853 -------------+------------------------------ Adj R-squared = 0.9835 Total | .246295255 27 .009122046 Root MSE = .01227 ------------------------------------------------------------------------------ lnyp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lnkdl | .3875059 .1375428 2.82 0.010 .1036316 .6713802 lkdlsq | .1482818 .1655295 0.90 0.379 -.1933543 .4899179 lnlpc1 | 1.640536 .1302826 12.59 0.000 1.371646 1.909426 _cons | -.0452076 .0201942 -2.24 0.035 -.0868863 -.0035289 -------------+---------------------------------------------------------------- rho | .69077 ------------------------------------------------------------------------------ Durbin-Watson statistic (original) 0.747043 Durbin-Watson statistic (transformed) 1.873340 . * eq 6 . regress lnyp time lnkpc1 lnlpc1 Source | SS df MS Number of obs = 28 -------------+------------------------------ F( 3, 24) = 4768.89 Model | 2.53877263 3 .846257544 Prob > F = 0.0000 Residual | .004258893 24 .000177454 R-squared = 0.9983 -------------+------------------------------ Adj R-squared = 0.9981 Total | 2.54303152 27 .094186353 Root MSE = .01332 ------------------------------------------------------------------------------ lnyp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- time | .0193107 .0056663 3.41 0.002 .0076159 .0310055 lnkpc1 | .1665722 .1525464 1.09 0.286 -.1482681 .4814124 lnlpc1 | .7186803 .1950445 3.68 0.001 .3161282 1.121232 _cons | -.0664215 .0105546 -6.29 0.000 -.0882052 -.0446378 ------------------------------------------------------------------------------ . estat dwatson Durbin-Watson d-statistic( 4, 28) = .7861833 . prais lnyp time lnkpc1 lnlpc1 Iteration 0: rho = 0.0000 Iteration 1: rho = 0.5828 Iteration 2: rho = 0.6618 Iteration 3: rho = 0.6769 Iteration 4: rho = 0.6800 Iteration 5: rho = 0.6806 Iteration 6: rho = 0.6808 Iteration 7: rho = 0.6808 Iteration 8: rho = 0.6808 Iteration 9: rho = 0.6808 Iteration 10: rho = 0.6808 Prais-Winsten AR(1) regression -- iterated estimates Source | SS df MS Number of obs = 28 -------------+------------------------------ F( 3, 24) = 785.35 Model | .259356882 3 .086452294 Prob > F = 0.0000 Residual | .002641943 24 .000110081 R-squared = 0.9899 -------------+------------------------------ Adj R-squared = 0.9887 Total | .261998825 27 .00970366 Root MSE = .01049 ------------------------------------------------------------------------------ lnyp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- time | .01477 .0046795 3.16 0.004 .0051121 .024428 lnkpc1 | .2221584 .113308 1.96 0.062 -.0116977 .4560146 lnlpc1 | .8938161 .2057184 4.34 0.000 .4692341 1.318398 _cons | -.0723672 .0136871 -5.29 0.000 -.100616 -.0441184 -------------+---------------------------------------------------------------- rho | .6808141 ------------------------------------------------------------------------------ Durbin-Watson statistic (original) 0.786183 Durbin-Watson statistic (transformed) 2.075562 . * eq 7 . regress lnyp cadmb1 lnkpc1 lnlpc1 Source | SS df MS Number of obs = 28 -------------+------------------------------ F( 3, 24) = 4105.80 Model | 2.53808616 3 .846028721 Prob > F = 0.0000 Residual | .004945362 24 .000206057 R-squared = 0.9981 -------------+------------------------------ Adj R-squared = 0.9978 Total | 2.54303152 27 .094186353 Root MSE = .01435 ------------------------------------------------------------------------------ lnyp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- cadmb1 | -.2383033 .0922676 -2.58 0.016 -.4287342 -.0478724 lnkpc1 | .6038341 .0851273 7.09 0.000 .4281399 .7795283 lnlpc1 | .9889896 .2143551 4.61 0.000 .5465824 1.431397 _cons | .0696966 .0450685 1.55 0.135 -.0233202 .1627133 ------------------------------------------------------------------------------ . estat dwatson Durbin-Watson d-statistic( 4, 28) = .6926076 . prais lnyp cadmb1 lnkpc1 lnlpc1 Iteration 0: rho = 0.0000 Iteration 1: rho = 0.6320 Iteration 2: rho = 0.6825 Iteration 3: rho = 0.6858 Iteration 4: rho = 0.6860 Iteration 5: rho = 0.6860 Iteration 6: rho = 0.6860 Prais-Winsten AR(1) regression -- iterated estimates Source | SS df MS Number of obs = 28 -------------+------------------------------ F( 3, 24) = 720.01 Model | .250963738 3 .083654579 Prob > F = 0.0000 Residual | .002788443 24 .000116185 R-squared = 0.9890 -------------+------------------------------ Adj R-squared = 0.9876 Total | .253752181 27 .009398229 Root MSE = .01078 ------------------------------------------------------------------------------ lnyp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- cadmb1 | -.1812493 .0634227 -2.86 0.009 -.3121473 -.0503514 lnkpc1 | .5469718 .0836411 6.54 0.000 .3743451 .7195985 lnlpc1 | 1.109508 .1923179 5.77 0.000 .7125833 1.506433 _cons | .0343039 .0346051 0.99 0.331 -.0371176 .1057253 -------------+---------------------------------------------------------------- rho | .6860024 ------------------------------------------------------------------------------ Durbin-Watson statistic (original) 0.692608 Durbin-Watson statistic (transformed) 1.829675 . * eq 8 . regress lnyp cadmb1 lnkpc1 lnlpc1 time Source | SS df MS Number of obs = 28 -------------+------------------------------ F( 4, 23) = 3876.73 Model | 2.53926526 4 .634816315 Prob > F = 0.0000 Residual | .003766263 23 .000163751 R-squared = 0.9985 -------------+------------------------------ Adj R-squared = 0.9983 Total | 2.54303152 27 .094186353 Root MSE = .0128 ------------------------------------------------------------------------------ lnyp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- cadmb1 | -.1529586 .088187 -1.73 0.096 -.3353874 .0294702 lnkpc1 | .2458094 .153494 1.60 0.123 -.0717171 .5633359 lnlpc1 | .8367058 .1993364 4.20 0.000 .424347 1.249064 time | .0156601 .0058359 2.68 0.013 .0035875 .0277326 _cons | .0109613 .045752 0.24 0.813 -.0836839 .1056065 ------------------------------------------------------------------------------ . estat dwatson Durbin-Watson d-statistic( 5, 28) = .7329918 . prais lnyp cadmb1 lnkpc1 lnlpc1 time Iteration 0: rho = 0.0000 Iteration 1: rho = 0.6043 Iteration 2: rho = 0.6780 Iteration 3: rho = 0.6909 Iteration 4: rho = 0.6934 Iteration 5: rho = 0.6940 Iteration 6: rho = 0.6941 Iteration 7: rho = 0.6941 Iteration 8: rho = 0.6941 Iteration 9: rho = 0.6941 Prais-Winsten AR(1) regression -- iterated estimates Source | SS df MS Number of obs = 28 -------------+------------------------------ F( 4, 23) = 630.54 Model | .238975959 4 .05974399 Prob > F = 0.0000 Residual | .002179249 23 .00009475 R-squared = 0.9910 -------------+------------------------------ Adj R-squared = 0.9894 Total | .241155207 27 .008931674 Root MSE = .00973 ------------------------------------------------------------------------------ lnyp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- cadmb1 | -.1328979 .0601651 -2.21 0.037 -.257359 -.0084368 lnkpc1 | .3273992 .1148091 2.85 0.009 .0898986 .5648999 lnlpc1 | .9087286 .1910424 4.76 0.000 .5135273 1.30393 time | .0115562 .0045605 2.53 0.019 .002122 .0209903 _cons | -.0023509 .0343866 -0.07 0.946 -.073485 .0687832 -------------+---------------------------------------------------------------- rho | .6941002 ------------------------------------------------------------------------------ Durbin-Watson statistic (original) 0.732992 Durbin-Watson statistic (transformed) 1.909962 . * eq 9 . regress lnyp cadmb cadmb1 cadmb2 lnkpc1 lnlpc1 time Source | SS df MS Number of obs = 28 -------------+------------------------------ F( 6, 21) = 2450.00 Model | 2.5394038 6 .423233966 Prob > F = 0.0000 Residual | .003627725 21 .000172749 R-squared = 0.9986 -------------+------------------------------ Adj R-squared = 0.9982 Total | 2.54303152 27 .094186353 Root MSE = .01314 ------------------------------------------------------------------------------ lnyp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- cadmb | -.0574073 .0896984 -0.64 0.529 -.2439454 .1291308 cadmb1 | -.1332209 .0933555 -1.43 0.168 -.3273643 .0609224 cadmb2 | -.0900901 .1079213 -0.83 0.413 -.3145248 .1343446 lnkpc1 | .2063809 .166697 1.24 0.229 -.1402844 .5530462 lnlpc1 | .9734762 .256206 3.80 0.001 .4406666 1.506286 time | .0161337 .0060691 2.66 0.015 .0035123 .0287551 _cons | .0691597 .0811588 0.85 0.404 -.0996192 .2379385 ------------------------------------------------------------------------------ . estat dwatson Durbin-Watson d-statistic( 7, 28) = .878633 . prais lnyp cadmb cadmb1 cadmb2 lnkpc1 lnlpc1 time Iteration 0: rho = 0.0000 Iteration 1: rho = 0.5274 Iteration 2: rho = 0.6786 Iteration 3: rho = 0.7261 Iteration 4: rho = 0.7420 Iteration 5: rho = 0.7476 Iteration 6: rho = 0.7495 Iteration 7: rho = 0.7502 Iteration 8: rho = 0.7504 Iteration 9: rho = 0.7505 Iteration 10: rho = 0.7505 Iteration 11: rho = 0.7505 Iteration 12: rho = 0.7506 Iteration 13: rho = 0.7506 Iteration 14: rho = 0.7506 Prais-Winsten AR(1) regression -- iterated estimates Source | SS df MS Number of obs = 28 -------------+------------------------------ F( 6, 21) = 264.99 Model | .16048512 6 .02674752 Prob > F = 0.0000 Residual | .002119689 21 .000100938 R-squared = 0.9870 -------------+------------------------------ Adj R-squared = 0.9832 Total | .16260481 27 .0060224 Root MSE = .01005 ------------------------------------------------------------------------------ lnyp | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- cadmb | .0341294 .0675706 0.51 0.619 -.1063913 .1746501 cadmb1 | -.1157445 .0644037 -1.80 0.087 -.2496793 .0181902 cadmb2 | .0606652 .0765398 0.79 0.437 -.098508 .2198384 lnkpc1 | .346698 .1180632 2.94 0.008 .1011722 .5922239 lnlpc1 | .8103476 .2348388 3.45 0.002 .3219735 1.298722 time | .0115428 .0046595 2.48 0.022 .0018529 .0212327 _cons | -.056675 .0795517 -0.71 0.484 -.2221118 .1087618 -------------+---------------------------------------------------------------- rho | .7505555 ------------------------------------------------------------------------------ Durbin-Watson statistic (original) 0.878633 Durbin-Watson statistic (transformed) 1.883058 . . end of do-file