{smcl} {com}{sf}{ul off}{txt}{.-} log: {res}P:\EUP Journal Article\Final Version and Data Files\EUP Journal Article OUTPUT LOG Year-On-Year FINAL.smcl {txt}log type: {res}smcl {txt}opened on: {res} 8 Dec 2011, 14:04:05 {com}. tsset ccode year, yearly {res}{txt}{col 8}panel variable: {res}ccode (strongly balanced) {txt}{col 9}time variable: {res}{col 25}year, 1979 to 2007 {txt}{col 17}delta: {res}1 year {com}. **TESTING FOR PANEL HETEROSKEDASTICITY** . . xtgls diffpubmanwagegrowth maastricht weightednai diffpubmanprogrowth L1.diffpubmanemploygrowth L1.NetGovtBorrowing L1.changeexportshare changefdi rightlegseats cent timetrend aus aut bel den fin fra ger ire ita jap neth swe uk can port spa, igls panels(heteroskedastic) {txt}Iteration 1: tolerance = {res}.38515831 {txt}Iteration 2: tolerance = {res}.1380349 {txt}Iteration 3: tolerance = {res}.0328237 {txt}Iteration 4: tolerance = {res}.00679593 {txt}Iteration 5: tolerance = {res}.00137502 {txt}Iteration 6: tolerance = {res}.00028244 {txt}Iteration 7: tolerance = {res}.00005985 {txt}Iteration 8: tolerance = {res}.0000132 {txt}Iteration 9: tolerance = {res}3.043e-06 {txt}Iteration 10: tolerance = {res}8.207e-07 {txt}Iteration 11: tolerance = {res}2.449e-07 {txt}Iteration 12: tolerance = {res}7.319e-08 {txt}Cross-sectional time-series FGLS regression Coefficients: {res}generalized least squares {txt}Panels: {res}heteroskedastic {txt}Correlation: {res}no autocorrelation {txt}Estimated covariances{col 28}= {res} 17{txt}{col 49}Number of obs{col 68}= {res} 377 {txt}Estimated autocorrelations{col 28}= {res} 0{txt}{col 49}Number of groups{col 68}= {res} 17 {txt}Estimated coefficients{col 28}= {res} 27{txt}{col 49}Obs per group: min{col 68}= {res} 4 {txt}{col 64}avg{col 68}= {res} 22.17647 {txt}{col 64}max{col 68}= {res} 26 {txt}{col 49}Wald chi2({res}26{txt}){col 68}= {res} 110.57 {txt}Log likelihood{col 28}= {res}-820.5556{txt}{col 49}Prob > chi2{col 68}= {res} 0.0000 {txt}{hline 13}{c TT}{hline 64} diff~egrowth {c |} Coef. Std. Err. z P>|z| [95% Conf. Interval] {hline 13}{c +}{hline 64} maastricht {c |} {res} .2236777 .3667826 0.61 0.542 -.4952031 .9425585 {txt}weightednai {c |} {res}-.0783061 3.464295 -0.02 0.982 -6.868201 6.711588 {txt}diff~ogrowth {c |} {res} .2569274 .0342027 7.51 0.000 .1898914 .3239634 {txt}diff~ygrowth {c |} L1. {c |} {res} .0212449 .0391663 0.54 0.588 -.0555197 .0980096 {txt}NetGovtBor~g {c |} L1. {c |} {res} .0356608 .041786 0.85 0.393 -.0462383 .1175598 {txt}changeexpo~e {c |} L1. {c |} {res} .0020942 .0188779 0.11 0.912 -.0349059 .0390942 {txt}changefdi {c |} {res} .0003734 .0058822 0.06 0.949 -.0111556 .0119024 {txt}rightlegse~s {c |} {res} .0052076 .0104086 0.50 0.617 -.0151929 .0256082 {txt}cent {c |} {res} 5.526797 2.721395 2.03 0.042 .1929605 10.86063 {txt}timetrend {c |} {res} .013608 .0155962 0.87 0.383 -.0169599 .0441759 {txt}aus {c |} {res}-1.630988 1.180154 -1.38 0.167 -3.944048 .6820706 {txt}aut {c |} {res}-4.407193 1.618894 -2.72 0.006 -7.580168 -1.234219 {txt}bel {c |} {res}-2.210408 .8842362 -2.50 0.012 -3.943479 -.4773372 {txt}den {c |} {res}-2.436062 .7112013 -3.43 0.001 -3.829991 -1.042133 {txt}fin {c |} {res}-1.487058 .7626453 -1.95 0.051 -2.981815 .0076995 {txt}fra {c |} {res}-1.577131 .6317575 -2.50 0.013 -2.815353 -.3389088 {txt}ger {c |} {res}-3.185496 .6670901 -4.78 0.000 -4.492968 -1.878023 {txt}ire {c |} {res}-1.176223 1.388307 -0.85 0.397 -3.897255 1.54481 {txt}ita {c |} {res}-.6785966 .8943629 -0.76 0.448 -2.431516 1.074323 {txt}jap {c |} {res}-.7163738 .611635 -1.17 0.242 -1.915156 .4824087 {txt}neth {c |} {res}-3.297589 .9620834 -3.43 0.001 -5.183238 -1.41194 {txt}swe {c |} {res}-2.128351 .9436856 -2.26 0.024 -3.977941 -.2787615 {txt}uk {c |} {res}-1.358441 .66134 -2.05 0.040 -2.654644 -.0622389 {txt}can {c |} {res}-.8037655 .5729192 -1.40 0.161 -1.926667 .3191354 {txt}port {c |} {res}-.8062693 1.01301 -0.80 0.426 -2.791733 1.179195 {txt}spa {c |} {res}-1.203788 .6842805 -1.76 0.079 -2.544953 .1373773 {txt}_cons {c |} {res}-.5158552 1.344834 -0.38 0.701 -3.151681 2.119971 {txt}{hline 13}{c BT}{hline 64} {com}. . estimates store hetero . . xtgls diffpubmanwagegrowth maastricht weightednai diffpubmanprogrowth L1.diffpubmanemploygrowth L1.NetGovtBorrowing L1.changeexportshare changefdi rightlegseats cent timetrend aus aut bel den fin fra ger ire ita jap neth swe uk can port spa {txt}Cross-sectional time-series FGLS regression Coefficients: {res}generalized least squares {txt}Panels: {res}homoskedastic {txt}Correlation: {res}no autocorrelation {txt}Estimated covariances{col 28}= {res} 1{txt}{col 49}Number of obs{col 68}= {res} 377 {txt}Estimated autocorrelations{col 28}= {res} 0{txt}{col 49}Number of groups{col 68}= {res} 17 {txt}Estimated coefficients{col 28}= {res} 27{txt}{col 49}Obs per group: min{col 68}= {res} 4 {txt}{col 64}avg{col 68}= {res} 22.17647 {txt}{col 64}max{col 68}= {res} 26 {txt}{col 49}Wald chi2({res}26{txt}){col 68}= {res} 132.10 {txt}Log likelihood{col 28}= {res}-894.1237{txt}{col 49}Prob > chi2{col 68}= {res} 0.0000 {txt}{hline 13}{c TT}{hline 64} diff~egrowth {c |} Coef. Std. Err. z P>|z| [95% Conf. Interval] {hline 13}{c +}{hline 64} maastricht {c |} {res} .5534187 .5233636 1.06 0.290 -.4723551 1.579192 {txt}weightednai {c |} {res} -4.16367 4.441099 -0.94 0.348 -12.86807 4.540725 {txt}diff~ogrowth {c |} {res} .3799793 .0394194 9.64 0.000 .3027187 .45724 {txt}diff~ygrowth {c |} L1. {c |} {res} .0298595 .0457553 0.65 0.514 -.0598193 .1195383 {txt}NetGovtBor~g {c |} L1. {c |} {res} .09065 .051011 1.78 0.076 -.0093296 .1906297 {txt}changeexpo~e {c |} L1. {c |} {res}-.0055805 .0292499 -0.19 0.849 -.0629092 .0517483 {txt}changefdi {c |} {res}-.0007475 .0080169 -0.09 0.926 -.0164603 .0149654 {txt}rightlegse~s {c |} {res}-.0119971 .0128117 -0.94 0.349 -.0371076 .0131133 {txt}cent {c |} {res} 6.485144 3.709688 1.75 0.080 -.7857119 13.756 {txt}timetrend {c |} {res} .0282685 .0209714 1.35 0.178 -.0128347 .0693718 {txt}aus {c |} {res}-2.318465 1.03496 -2.24 0.025 -4.34695 -.2899804 {txt}aut {c |} {res}-5.816381 2.183139 -2.66 0.008 -10.09526 -1.537506 {txt}bel {c |} {res} -3.41172 1.635324 -2.09 0.037 -6.616897 -.2065433 {txt}den {c |} {res}-3.220081 .9449334 -3.41 0.001 -5.072116 -1.368046 {txt}fin {c |} {res}-2.393958 .9605357 -2.49 0.013 -4.276573 -.5113425 {txt}fra {c |} {res}-1.930965 .8255438 -2.34 0.019 -3.549001 -.3129289 {txt}ger {c |} {res}-3.971802 .9697594 -4.10 0.000 -5.872495 -2.071108 {txt}ire {c |} {res}-.8605357 1.031069 -0.83 0.404 -2.881393 1.160322 {txt}ita {c |} {res}-1.636984 .9849086 -1.66 0.096 -3.567369 .2934017 {txt}jap {c |} {res}-.5807447 .7636981 -0.76 0.447 -2.077565 .916076 {txt}neth {c |} {res}-4.379101 1.296659 -3.38 0.001 -6.920506 -1.837697 {txt}swe {c |} {res}-2.972779 1.199543 -2.48 0.013 -5.32384 -.6217168 {txt}uk {c |} {res}-1.554987 .7262691 -2.14 0.032 -2.978448 -.1315254 {txt}can {c |} {res}-1.216544 .7820224 -1.56 0.120 -2.74928 .3161921 {txt}port {c |} {res}-2.077775 1.2936 -1.61 0.108 -4.613185 .4576347 {txt}spa {c |} {res}-2.387158 1.086551 -2.20 0.028 -4.516759 -.257558 {txt}_cons {c |} {res} 1.050205 1.80919 0.58 0.562 -2.495743 4.596153 {txt}{hline 13}{c BT}{hline 64} {com}. . local df = e(N_g) - 1 . . lrtest hetero . , df(`df') {txt}Likelihood-ratio test{col 56}LR chi2({res}16{txt}){col 68}={res} 147.14 {txt}(Assumption: {res}{stata est replay .:.}{txt} nested in {res}{stata est replay hetero:hetero}{txt}){col 56}Prob > chi2 = {res} 0.0000 {com}. . **TESTING FOR SERIAL CORRELATION** . . xtserial diffpubmanwagegrowth maastricht weightednai diffpubmanprogrowth lagdiffemploymentgrowth lagborrowing lagexports changefdi rightlegseats cent timetrend aus aut bel den fin fra ger ire ita jap neth swe uk can port spa {txt}Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation {col 5}F( 1, 15) = {res} 5.275 {txt}{col 12}Prob > F = {res} 0.0365 {com}. . ***RESULTS FOR YEAR-ON-YEAR MODELS, TABLE 1** . . **MODEL 1** . . xtpcse diffpubmanwagegrowth maastricht emu diffpubmanprogrowth L1.diffpubmanemploygrowth L1.NetGovtBorrowing L1.changeexportshare changefdi rightlegseats cent timetrend aus aut bel den fin fra ger ire ita jap neth swe uk can port spa, corr(psar) {txt}Number of gaps in sample: {res}6 {txt}(note: computations for rho restarted at each gap) (note: estimates of rho outside [-1,1] bounded to be in the range [-1,1]) (note: the number of observations per panel, e(n_sigma) = 1, used to compute the disturbance of covariance matrix e(Sigma) is less than half of the average number of observations per panel, e(n_avg) = 22.176471; you may want to consider the pairwise option) Prais-Winsten regression, correlated panels corrected standard errors (PCSEs) Group variable:{col 19}{res}ccode{col 49}{txt}Number of obs{col 68}= {res} 377 {txt}Time variable:{col 19}{res}year{col 49}{txt}Number of groups{col 68}= {res} 17 {txt}Panels:{col 19}{res}correlated (unbalanced){col 49}{txt}Obs per group: min{col 68}= {res} 4 {txt}Autocorrelation:{col 19}{res}panel-specific AR(1){col 64}{txt}avg{col 68}= {res} 22.17647 {txt}Sigma computed by {col 19}{res}casewise selection{txt}{col 64}max{col 68}= {res} 26 {txt}Estimated covariances{col 28}= {res} 153{col 49}{txt}R-squared{col 68}= {res} 0.2788 {txt}Estimated autocorrelations{col 28}= {res} 17{col 49}{txt}Wald chi2({res}17{txt}){col 68}= {res} 3.32e+06 {txt}Estimated coefficients{col 28}= {res} 27{col 49}{txt}Prob > chi2{col 68}= {res} 0.0000 {txt}{hline 13}{c TT}{hline 64} {c |} Panel-corrected diff~egrowth {c |} Coef. Std. Err. z P>|z| [95% Conf. Interval] {hline 13}{c +}{hline 64} maastricht {c |} {res} .2443543 .2533931 0.96 0.335 -.2522871 .7409957 {txt}emu {c |} {res} .6130743 .3376173 1.82 0.069 -.0486434 1.274792 {txt}diff~ogrowth {c |} {res} .385745 .0356364 10.82 0.000 .3158989 .4555911 {txt}diff~ygrowth {c |} L1. {c |} {res} .0249513 .0331859 0.75 0.452 -.0400919 .0899945 {txt}NetGovtBor~g {c |} L1. {c |} {res} .0478858 .0221451 2.16 0.031 .0044822 .0912895 {txt}changeexpo~e {c |} L1. {c |} {res} .0006178 .0194441 0.03 0.975 -.0374919 .0387275 {txt}changefdi {c |} {res} -.001763 .0031801 -0.55 0.579 -.0079958 .0044698 {txt}rightlegse~s {c |} {res}-.0090995 .0059635 -1.53 0.127 -.0207878 .0025889 {txt}cent {c |} {res} 5.233724 1.980234 2.64 0.008 1.352536 9.114912 {txt}timetrend {c |} {res} .0036853 .0173648 0.21 0.832 -.030349 .0377197 {txt}aus {c |} {res}-1.901479 .6241979 -3.05 0.002 -3.124885 -.6780738 {txt}aut {c |} {res}-4.862663 1.158444 -4.20 0.000 -7.133172 -2.592154 {txt}bel {c |} {res}-2.827235 .7449088 -3.80 0.000 -4.287229 -1.36724 {txt}den {c |} {res} -2.84155 .3698981 -7.68 0.000 -3.566537 -2.116563 {txt}fin {c |} {res}-1.727822 .8027753 -2.15 0.031 -3.301233 -.1544118 {txt}fra {c |} {res}-1.914687 .2558489 -7.48 0.000 -2.416141 -1.413232 {txt}ger {c |} {res}-3.708429 .6170387 -6.01 0.000 -4.917803 -2.499055 {txt}ire {c |} {res}-1.027784 1.601321 -0.64 0.521 -4.166315 2.110747 {txt}ita {c |} {res} -1.42922 .5198356 -2.75 0.006 -2.448079 -.4103606 {txt}jap {c |} {res}-.6321568 .4156219 -1.52 0.128 -1.446761 .1824471 {txt}neth {c |} {res}-3.898953 .714597 -5.46 0.000 -5.299537 -2.498369 {txt}swe {c |} {res}-2.268926 1.214124 -1.87 0.062 -4.648565 .1107129 {txt}uk {c |} {res}-1.442312 .3212238 -4.49 0.000 -2.071899 -.8127253 {txt}can {c |} {res}-1.179956 .3607874 -3.27 0.001 -1.887086 -.4728256 {txt}port {c |} {res}-1.069056 .6640147 -1.61 0.107 -2.370501 .2323885 {txt}spa {c |} {res}-2.091022 .5117769 -4.09 0.000 -3.094086 -1.087958 {txt}_cons {c |} {res} .977614 .9426277 1.04 0.300 -.8699024 2.82513 {txt}{hline 13}{c BT}{hline 64} rhos = {res}-.0679804{txt} {res} .2178766{txt} {res}-.0837541{txt} {res} .2672649{txt} {res} .1819162{txt} ... {res} .396531 {txt}{hline 78} {com}. . **WALD TEST FOR MODEL 1 BETWEEN EMU AND MAASTRICHT DUMMY** . . test maastricht=emu {txt} ( 1) {res}maastricht - emu = 0 {txt}{col 12}chi2( 1) ={res} 6.73 {txt}{col 10}Prob > chi2 = {res} 0.0095 {com}. . **MODEL 2** . . xtpcse diffpubmanwagegrowth maastricht weightednai diffpubmanprogrowth L1.diffpubmanemploygrowth L1.NetGovtBorrowing L1.changeexportshare changefdi rightlegseats cent timetrend aus aut bel den fin fra ger ire ita jap neth swe uk can port spa, corr(psar) {txt}Number of gaps in sample: {res}6 {txt}(note: computations for rho restarted at each gap) (note: estimates of rho outside [-1,1] bounded to be in the range [-1,1]) (note: the number of observations per panel, e(n_sigma) = 1, used to compute the disturbance of covariance matrix e(Sigma) is less than half of the average number of observations per panel, e(n_avg) = 22.176471; you may want to consider the pairwise option) Prais-Winsten regression, correlated panels corrected standard errors (PCSEs) Group variable:{col 19}{res}ccode{col 49}{txt}Number of obs{col 68}= {res} 377 {txt}Time variable:{col 19}{res}year{col 49}{txt}Number of groups{col 68}= {res} 17 {txt}Panels:{col 19}{res}correlated (unbalanced){col 49}{txt}Obs per group: min{col 68}= {res} 4 {txt}Autocorrelation:{col 19}{res}panel-specific AR(1){col 64}{txt}avg{col 68}= {res} 22.17647 {txt}Sigma computed by {col 19}{res}casewise selection{txt}{col 64}max{col 68}= {res} 26 {txt}Estimated covariances{col 28}= {res} 153{col 49}{txt}R-squared{col 68}= {res} 0.2828 {txt}Estimated autocorrelations{col 28}= {res} 17{col 49}{txt}Wald chi2({res}17{txt}){col 68}= {res} 3.73e+07 {txt}Estimated coefficients{col 28}= {res} 27{col 49}{txt}Prob > chi2{col 68}= {res} 0.0000 {txt}{hline 13}{c TT}{hline 64} {c |} Panel-corrected diff~egrowth {c |} Coef. Std. Err. z P>|z| [95% Conf. Interval] {hline 13}{c +}{hline 64} maastricht {c |} {res} .280849 .1836595 1.53 0.126 -.079117 .6408149 {txt}weightednai {c |} {res}-5.919986 1.648118 -3.59 0.000 -9.150237 -2.689735 {txt}diff~ogrowth {c |} {res} .3880969 .035649 10.89 0.000 .318226 .4579677 {txt}diff~ygrowth {c |} L1. {c |} {res} .0248187 .0331019 0.75 0.453 -.0400597 .0896972 {txt}NetGovtBor~g {c |} L1. {c |} {res} .0536909 .0235612 2.28 0.023 .0075117 .0998701 {txt}changeexpo~e {c |} L1. {c |} {res}-.0019709 .0198787 -0.10 0.921 -.0409325 .0369906 {txt}changefdi {c |} {res}-.0017701 .0032608 -0.54 0.587 -.0081611 .0046209 {txt}rightlegse~s {c |} {res} -.009011 .0048487 -1.86 0.063 -.0185142 .0004923 {txt}cent {c |} {res} 5.104224 2.156256 2.37 0.018 .8780397 9.330409 {txt}timetrend {c |} {res} .0127284 .0111058 1.15 0.252 -.0090386 .0344954 {txt}aus {c |} {res}-2.119577 .6999726 -3.03 0.002 -3.491498 -.7476556 {txt}aut {c |} {res}-4.918423 1.186754 -4.14 0.000 -7.244419 -2.592427 {txt}bel {c |} {res}-3.109333 .6928746 -4.49 0.000 -4.467342 -1.751324 {txt}den {c |} {res}-3.002316 .3766883 -7.97 0.000 -3.740611 -2.26402 {txt}fin {c |} {res}-2.094977 .8672326 -2.42 0.016 -3.794722 -.3952327 {txt}fra {c |} {res}-2.078561 .3036839 -6.84 0.000 -2.67377 -1.483351 {txt}ger {c |} {res}-3.712959 .6707359 -5.54 0.000 -5.027577 -2.398341 {txt}ire {c |} {res}-1.293797 1.670752 -0.77 0.439 -4.568412 1.980817 {txt}ita {c |} {res}-1.793683 .6066744 -2.96 0.003 -2.982743 -.6046231 {txt}jap {c |} {res}-.7591395 .4783338 -1.59 0.113 -1.696656 .1783774 {txt}neth {c |} {res}-3.948455 .7224905 -5.47 0.000 -5.364511 -2.5324 {txt}swe {c |} {res}-2.538311 1.337878 -1.90 0.058 -5.160504 .0838818 {txt}uk {c |} {res}-1.552838 .4099171 -3.79 0.000 -2.356261 -.7494152 {txt}can {c |} {res}-1.394344 .4382481 -3.18 0.001 -2.253295 -.5353939 {txt}port {c |} {res}-1.355641 .6774808 -2.00 0.045 -2.683479 -.0278028 {txt}spa {c |} {res}-2.301221 .5424136 -4.24 0.000 -3.364332 -1.23811 {txt}_cons {c |} {res} 1.751234 1.071368 1.63 0.102 -.3486085 3.851077 {txt}{hline 13}{c BT}{hline 64} rhos = {res}-.0795559{txt} {res} .2137123{txt} {res}-.1261477{txt} {res} .282686{txt} {res} .2021417{txt} ... {res} .4560395 {txt}{hline 78} {com}. . **MODEL 3** . . xtpcse diffpubmanwagegrowth maastricht weightednai diffpubmanprogrowth L1.diffpubmanemploygrowth L1.NetGovtBorrowing L1.changeexportshare changefdi rightlegseats wcoord timetrend aus aut bel den fin fra ger ire ita jap neth swe uk can port spa, corr(psar) {txt}(note: the number of observations per panel, e(n_sigma) = 2, used to compute the disturbance of covariance matrix e(Sigma) is less than half of the average number of observations per panel, e(n_avg) = 23.411765; you may want to consider the pairwise option) Prais-Winsten regression, correlated panels corrected standard errors (PCSEs) Group variable:{col 19}{res}ccode{col 49}{txt}Number of obs{col 68}= {res} 398 {txt}Time variable:{col 19}{res}year{col 49}{txt}Number of groups{col 68}= {res} 17 {txt}Panels:{col 19}{res}correlated (unbalanced){col 49}{txt}Obs per group: min{col 68}= {res} 4 {txt}Autocorrelation:{col 19}{res}panel-specific AR(1){col 64}{txt}avg{col 68}= {res} 23.41176 {txt}Sigma computed by {col 19}{res}casewise selection{txt}{col 64}max{col 68}= {res} 26 {txt}Estimated covariances{col 28}= {res} 153{col 49}{txt}R-squared{col 68}= {res} 0.2722 {txt}Estimated autocorrelations{col 28}= {res} 17{col 49}{txt}Wald chi2({res}20{txt}){col 68}= {res} 84681.52 {txt}Estimated coefficients{col 28}= {res} 27{col 49}{txt}Prob > chi2{col 68}= {res} 0.0000 {txt}{hline 13}{c TT}{hline 64} {c |} Panel-corrected diff~egrowth {c |} Coef. Std. Err. z P>|z| [95% Conf. Interval] {hline 13}{c +}{hline 64} maastricht {c |} {res}-.1060793 .3106656 -0.34 0.733 -.7149726 .502814 {txt}weightednai {c |} {res} -4.64544 1.962365 -2.37 0.018 -8.491604 -.7992748 {txt}diff~ogrowth {c |} {res} .3830409 .0330566 11.59 0.000 .3182511 .4478307 {txt}diff~ygrowth {c |} L1. {c |} {res} .0401324 .0388579 1.03 0.302 -.0360276 .1162925 {txt}NetGovtBor~g {c |} L1. {c |} {res} .0560215 .0343596 1.63 0.103 -.0113221 .123365 {txt}changeexpo~e {c |} L1. {c |} {res} .0134178 .0176349 0.76 0.447 -.021146 .0479817 {txt}changefdi {c |} {res}-.0012399 .004881 -0.25 0.799 -.0108065 .0083268 {txt}rightlegse~s {c |} {res}-.0191295 .005708 -3.35 0.001 -.0303169 -.007942 {txt}wcoord {c |} {res} .4454635 .1571411 2.83 0.005 .1374726 .7534545 {txt}timetrend {c |} {res} .0096318 .013837 0.70 0.486 -.0174882 .0367518 {txt}aus {c |} {res}-2.114493 .7542232 -2.80 0.005 -3.592743 -.636243 {txt}aut {c |} {res}-3.455968 .7455507 -4.64 0.000 -4.917221 -1.994716 {txt}bel {c |} {res}-3.448539 .9257195 -3.73 0.000 -5.262916 -1.634163 {txt}den {c |} {res}-3.176037 .39057 -8.13 0.000 -3.94154 -2.410534 {txt}fin {c |} {res}-2.776312 .7590009 -3.66 0.000 -4.263927 -1.288698 {txt}fra {c |} {res}-2.508571 .8007677 -3.13 0.002 -4.078047 -.9390948 {txt}ger {c |} {res}-3.977913 .6238994 -6.38 0.000 -5.200734 -2.755093 {txt}ire {c |} {res}-1.330085 1.223923 -1.09 0.277 -3.72893 1.068761 {txt}ita {c |} {res}-2.328207 .9370445 -2.48 0.013 -4.16478 -.491633 {txt}jap {c |} {res}-2.261033 .5811566 -3.89 0.000 -3.400079 -1.121987 {txt}neth {c |} {res}-3.836295 .5784963 -6.63 0.000 -4.970127 -2.702463 {txt}swe {c |} {res}-2.472399 .9548231 -2.59 0.010 -4.343818 -.60098 {txt}uk {c |} {res}-1.480683 .4340831 -3.41 0.001 -2.33147 -.6298955 {txt}can {c |} {res}-1.437362 .7028382 -2.05 0.041 -2.8149 -.0598249 {txt}port {c |} {res}-2.236957 .8298528 -2.70 0.007 -3.863438 -.6104752 {txt}spa {c |} {res}-2.740167 .6182039 -4.43 0.000 -3.951824 -1.528509 {txt}_cons {c |} {res} 2.99819 .8174934 3.67 0.000 1.395932 4.600448 {txt}{hline 13}{c BT}{hline 64} rhos = {res}-.0333061{txt} {res} .2519194{txt} {res}-.1784455{txt} {res} .2717103{txt} {res} .1126737{txt} ... {res} .5146096 {txt}{hline 78} {com}. log close {txt}log: {res}P:\EUP Journal Article\Final Version and Data Files\EUP Journal Article OUTPUT LOG Year-On-Year FINAL.smcl {txt}log type: {res}smcl {txt}closed on: {res} 8 Dec 2011, 14:05:01 {txt}{.-} {smcl} {txt}{sf}{ul off}