MODEL 1A
--> REGRESS;Lhs=lnep;Rhs=ONE, compulso, arrival, concurr, seatshar, restday, ...
    ,constitu, netpayer, netrecei$

+-----------------------------------------------------------------------+
| Ordinary    least squares regression    Weighting variable = none     |
| Dep. var. = LNEP     Mean=   .5234934857    , S.D.=   .9388390734     |
| Model size: Observations =      64, Parameters =  10, Deg.Fr.=     54 |
| Residuals:  Sum of squares= 9.766228328    , Std.Dev.=         .42527 |
| Fit:        R-squared=  .824125, Adjusted R-squared =          .79481 |
| Model test: F[  9,     54] =   28.12,    Prob value =          .00000 |
| Diagnostic: Log-L =    -30.6536, Restricted(b=0) Log-L =     -86.2690 |
|             LogAmemiyaPrCrt.=   -1.565, Akaike Info. Crt.=      1.270 |
| Autocorrel: Durbin-Watson Statistic =   1.09979,   Rho =       .45010 |
+-----------------------------------------------------------------------+
+---------+--------------+----------------+--------+---------+----------+
|Variable | Coefficient  | Standard Error |t-ratio |P[|T|>t] | Mean of X|
+---------+--------------+----------------+--------+---------+----------+
 Constant -.3106464100      .14082711       -2.206   .0317
 COMPULSO  1.224447945      .17027337        7.191   .0000  .28125000
 ARRIVAL   .8963516067E-03  .20935305         .004   .9966  .93750000E-01
 CONCURR   .4284986239      .14446750        2.966   .0045  .26562500
 SEATSHAR  .1969184716E-01  .16860132E-01    1.168   .2480  8.2995483
 RESTDAY   .6399315287      .16027420        3.993   .0002  .68750000
 LIST     -.5642262840      .20577312       -2.742   .0083  .43750000
 CONSTITU  .9454306491E-01  .17400070         .543   .5891  .31250000
 NETPAYER -.1592396331      .15709063       -1.014   .3153  .18750000
 NETRECEI  .6981437013E-01  .18670003         .374   .7099  .28125000

MODEL 1B

--> REGRESS;Lhs=lnep;Rhs=ONE, compulso, arrival, concurr, seatshar, restday, ...
    ,constitu, netpayer, netrecei,  y1999,uk, italcomp, greece, ireland, finland
    , sweden, france$

+-----------------------------------------------------------------------+
| Ordinary    least squares regression    Weighting variable = none     |
| Dep. var. = LNEP     Mean=   .5234934857    , S.D.=   .9388390734     |
| Model size: Observations =      64, Parameters =  18, Deg.Fr.=     46 |
| Residuals:  Sum of squares= 2.342300872    , Std.Dev.=         .22565 |
| Fit:        R-squared=  .957819, Adjusted R-squared =          .94223 |
| Model test: F[ 17,     46] =   61.44,    Prob value =          .00000 |
| Diagnostic: Log-L =     15.0359, Restricted(b=0) Log-L =     -86.2690 |
|             LogAmemiyaPrCrt.=   -2.730, Akaike Info. Crt.=       .093 |
| Autocorrel: Durbin-Watson Statistic =   2.01335,   Rho =      -.00668 |
+-----------------------------------------------------------------------+
+---------+--------------+----------------+--------+---------+----------+
|Variable | Coefficient  | Standard Error |t-ratio |P[|T|>t] | Mean of X|
+---------+--------------+----------------+--------+---------+----------+
 Constant -.1551573026      .10733574       -1.446   .1551
 COMPULSO  1.520633228      .21527747        7.064   .0000  .28125000
 ARRIVAL   .2316629623      .13764282        1.683   .0991  .93750000E-01
 CONCURR   .4082080127      .95953081E-01    4.254   .0001  .26562500
 SEATSHAR  .3767162851E-01  .16960306E-01    2.221   .0313  8.2995483
 RESTDAY   .4120888358      .16039394        2.569   .0135  .68750000
 LIST     -.2384888878      .25172900        -.947   .3484  .43750000
 CONSTITU  .3280713194      .18503370        1.773   .0828  .31250000
 NETPAYER -.3712288611      .13235064       -2.805   .0074  .18750000
 NETRECEI -.1999182618      .22822028        -.876   .3856  .28125000
 Y1999    -.2912158473      .80121462E-01   -3.635   .0007  .23437500
 UK       -1.252533502      .29230159       -4.285   .0001  .78125000E-01
 ITALCOMP -1.323495737      .25905617       -5.109   .0000  .46875000E-01
 GREECE   -.5395499348      .22106071       -2.441   .0186  .78125000E-01
 IRELAND  -.7140490344E-01  .28011057        -.255   .7999  .78125000E-01
 FINLAND  -.7460820942      .21466491       -3.476   .0011  .31250000E-01
 SWEDEN   -.3963434129      .23953044       -1.655   .1048  .31250000E-01
 FRANCE   -.4597390513      .19335269       -2.378   .0216  .78125000E-01

MODEL 2A

--> REGRESS;Lhs=lndiff;Rhs=ONE, compulso, arrival, concurr, seatshar, restday...
    ,constitu, netpayer, netrecei$

+-----------------------------------------------------------------------+
| Ordinary    least squares regression    Weighting variable = none     |
| Dep. var. = LNDIFF   Mean=  -.9672004209    , S.D.=   .6862341370     |
| Model size: Observations =      64, Parameters =  10, Deg.Fr.=     54 |
| Residuals:  Sum of squares= 6.267294576    , Std.Dev.=         .34068 |
| Fit:        R-squared=  .788751, Adjusted R-squared =          .75354 |
| Model test: F[  9,     54] =   22.40,    Prob value =          .00000 |
| Diagnostic: Log-L =    -16.4588, Restricted(b=0) Log-L =     -66.2098 |
|             LogAmemiyaPrCrt.=   -2.008, Akaike Info. Crt.=       .827 |
| Autocorrel: Durbin-Watson Statistic =   1.50429,   Rho =       .24785 |
+-----------------------------------------------------------------------+
+---------+--------------+----------------+--------+---------+----------+
|Variable | Coefficient  | Standard Error |t-ratio |P[|T|>t] | Mean of X|
+---------+--------------+----------------+--------+---------+----------+
 Constant -1.705332464      .11281397      -15.116   .0000
 COMPULSO  .3986861067      .13640282        2.923   .0051  .28125000
 ARRIVAL  -.3907268024E-01  .16770882        -.233   .8167  .93750000E-01
 CONCURR   .6483192756      .11573022        5.602   .0000  .26562500
 SEATSHAR -.3834436836E-03  .13506337E-01    -.028   .9775  8.2995483
 RESTDAY   .6431284278      .12839267        5.009   .0000  .68750000
 LIST     -.1093253504      .16484100        -.663   .5100  .43750000
 CONSTITU  .1428794931      .13938872        1.025   .3099  .31250000
 NETPAYER -.3457214485      .12584237       -2.747   .0082  .18750000
 NETRECEI  .3075172205      .14956191        2.056   .0446  .28125000

MODEL 2B
--> REGRESS;Lhs=lndiff;Rhs=ONE, compulso, arrival, concurr, seatshar, restday...
    ,constitu, netpayer, netrecei,  y1999, sweden, france, italcomp, luxembou$

+-----------------------------------------------------------------------+
| Ordinary    least squares regression    Weighting variable = none     |
| Dep. var. = LNDIFF   Mean=  -.9672004209    , S.D.=   .6862341370     |
| Model size: Observations =      64, Parameters =  15, Deg.Fr.=     49 |
| Residuals:  Sum of squares= 2.616126762    , Std.Dev.=         .23106 |
| Fit:        R-squared=  .911819, Adjusted R-squared =          .88662 |
| Model test: F[ 14,     49] =   36.19,    Prob value =          .00000 |
| Diagnostic: Log-L =     11.4980, Restricted(b=0) Log-L =     -66.2098 |
|             LogAmemiyaPrCrt.=   -2.720, Akaike Info. Crt.=       .109 |
| Autocorrel: Durbin-Watson Statistic =   2.12206,   Rho =      -.06103 |
+-----------------------------------------------------------------------+
+---------+--------------+----------------+--------+---------+----------+
|Variable | Coefficient  | Standard Error |t-ratio |P[|T|>t] | Mean of X|
+---------+--------------+----------------+--------+---------+----------+
 Constant -1.816453065      .96104663E-01  -18.901   .0000
 COMPULSO  .4290085328      .12400108        3.460   .0011  .28125000
 ARRIVAL   .2958932665      .13736826        2.154   .0362  .93750000E-01
 CONCURR   .4056140889      .96471606E-01    4.204   .0001  .26562500
 SEATSHAR  .7623145752E-02  .14976549E-01     .509   .6130  8.2995483
 RESTDAY   .6549689488      .96016210E-01    6.821   .0000  .68750000
 LIST     -.4159083745      .15487951       -2.685   .0099  .43750000
 CONSTITU  .3079206163      .10925051        2.818   .0069  .31250000
 NETPAYER -.3613216612E-01  .10868917        -.332   .7410  .18750000
 NETRECEI  .6073852239      .12693941        4.785   .0000  .28125000
 Y1999    -.1046247120      .78955263E-01   -1.325   .1913  .23437500
 SWEDEN   -1.012635224      .21064073       -4.807   .0000  .31250000E-01
 FRANCE    .6708386060      .14178485        4.731   .0000  .78125000E-01
 ITALCOMP -.4745674224      .23853587       -1.990   .0522  .46875000E-01
 LUXEMBOU  .3486425266      .17222468        2.024   .0484  .78125000E-01

MODEL 3A
--> REGRESS;Lhs=lnep;Rhs=ONE, compulso, arrival, concurr, seatshar, restday, ...
    ,constitu, netpayer, netrecei, lnnp$

+-----------------------------------------------------------------------+
| Ordinary    least squares regression    Weighting variable = none     |
| Dep. var. = LNEP     Mean=   .5234934857    , S.D.=   .9388390734     |
| Model size: Observations =      64, Parameters =  11, Deg.Fr.=     53 |
| Residuals:  Sum of squares= 5.689194062    , Std.Dev.=         .32763 |
| Fit:        R-squared=  .897546, Adjusted R-squared =          .87822 |
| Model test: F[ 10,     53] =   46.43,    Prob value =          .00000 |
| Diagnostic: Log-L =    -13.3620, Restricted(b=0) Log-L =     -86.2690 |
|             LogAmemiyaPrCrt.=   -2.073, Akaike Info. Crt.=       .761 |
| Autocorrel: Durbin-Watson Statistic =   1.44713,   Rho =       .27643 |
+-----------------------------------------------------------------------+
+---------+--------------+----------------+--------+---------+----------+
|Variable | Coefficient  | Standard Error |t-ratio |P[|T|>t] | Mean of X|
+---------+--------------+----------------+--------+---------+----------+
 Constant -1.323817048      .19697181       -6.721   .0000
 COMPULSO  .6245726931      .16334811        3.824   .0003  .28125000
 ARRIVAL  -.2813917891E-01  .16135610        -.174   .8622  .93750000E-01
 CONCURR   .5881874866      .11427533        5.147   .0000  .26562500
 SEATSHAR  .5108138401E-02  .13202978E-01     .387   .7004  8.2995483
 RESTDAY   .6422539182      .12347713        5.201   .0000  .68750000
 LIST     -.2337631898      .16735230       -1.397   .1683  .43750000
 CONSTITU  .1296570962      .13417259         .966   .3383  .31250000
 NETPAYER -.2947094754      .12300396       -2.396   .0201  .18750000
 NETRECEI  .2424937683      .14653890        1.655   .1039  .28125000
 LNNP      .7264506836      .11787492        6.163   .0000  1.4906939
MODEL 3B
--> REGRESS;Lhs=lnep;Rhs=ONE, compulso, arrival, concurr, seatshar, restday, ...
    ,constitu, netpayer, netrecei, lnnp, y1999, italcomp,uk, luxembou
    , sweden, finland$

+-----------------------------------------------------------------------+
| Ordinary    least squares regression    Weighting variable = none     |
| Dep. var. = LNEP     Mean=   .5234934857    , S.D.=   .9388390734     |
| Model size: Observations =      64, Parameters =  17, Deg.Fr.=     47 |
| Residuals:  Sum of squares= 1.907423027    , Std.Dev.=         .20145 |
| Fit:        R-squared=  .965650, Adjusted R-squared =          .95396 |
| Model test: F[ 16,     47] =   82.58,    Prob value =          .00000 |
| Diagnostic: Log-L =     21.6081, Restricted(b=0) Log-L =     -86.2690 |
|             LogAmemiyaPrCrt.=   -2.969, Akaike Info. Crt.=      -.144 |
| Autocorrel: Durbin-Watson Statistic =   2.18916,   Rho =      -.09458 |
+-----------------------------------------------------------------------+
+---------+--------------+----------------+--------+---------+----------+
|Variable | Coefficient  | Standard Error |t-ratio |P[|T|>t] | Mean of X|
+---------+--------------+----------------+--------+---------+----------+
 Constant -.9012058085      .16601088       -5.429   .0000
 COMPULSO  .7593860003      .13543804        5.607   .0000  .28125000
 ARRIVAL   .2502067365      .12835406        1.949   .0572  .93750000E-01
 CONCURR   .4234781520      .87978966E-01    4.813   .0000  .26562500
 SEATSHAR  .1288866651E-01  .13944681E-01     .924   .3601  8.2995483
 RESTDAY   .5719191422      .14075675        4.063   .0002  .68750000
 LIST     -.1698451917      .24699926        -.688   .4951  .43750000
 CONSTITU  .4685036901      .15468323        3.029   .0040  .31250000
 NETPAYER -.2500600004      .89209895E-01   -2.803   .0073  .18750000
 NETRECEI -.1365971110E-01  .15098265        -.090   .9283  .28125000
 LNNP      .4808670096      .90181401E-01    5.332   .0000  1.4906939
 Y1999    -.2209147777      .72972363E-01   -3.027   .0040  .23437500
 ITALCOMP -.7442312994      .21927429       -3.394   .0014  .46875000E-01
 UK       -.8367328093      .29839668       -2.804   .0073  .78125000E-01
 LUXEMBOU  .3312158931      .21472712        1.542   .1297  .78125000E-01
 SWEDEN   -.6963810649      .20787548       -3.350   .0016  .31250000E-01
 FINLAND  -.4884880921      .21175382       -2.307   .0255  .31250000E-01