Here I repeat the same analysis as in the paper but now the dependent variable is logit transformed to make sure that all model predictions are bouded by 0 and 1. If e is the turnout (in percentages) in the EP elections its logit is ln((e/100)/(1-(e/100))). Similar transformation is applied to the turnout in national parliamentary elections.
NOTE! in the following models (the "b" variants) the dummy variables may be different than in the paper. The reason is that I used the modelling strategy explained in the paper.
All in all, these results are very similar to the ones in the paper (with the untransformed variables).
Compulsory voting ++++++ Simultanous elections ++++++ Weekend voting ++++++ Country net payer ---- Country net receiver ++ Share of seats + Strict party lists -- Multiple constituencies ++ New member country +
--> 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
--> 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
--> REGRESS;Lhs=LNDIFF;Rhs=ONE, compulso, arrival, concurr, seatshar, restday...
,constitu, netpayer, netrecei$
+-----------------------------------------------------------------------+
| Ordinary least squares regression Weighting variable = none |
| Dep. var. = LNDIFF Mean= -.9717786972 , S.D.= .6841621581 |
| Model size: Observations = 64, Parameters = 10, Deg.Fr.= 54 |
| Residuals: Sum of squares= 5.989583614 , Std.Dev.= .33304 |
| Fit: R-squared= .796887, Adjusted R-squared = .76303 |
| Model test: F[ 9, 54] = 23.54, Prob value = .00000 |
| Diagnostic: Log-L = -15.0085, Restricted(b=0) Log-L = -66.0163 |
| LogAmemiyaPrCrt.= -2.054, Akaike Info. Crt.= .782 |
| Autocorrel: Durbin-Watson Statistic = 1.43528, Rho = .28236 |
+-----------------------------------------------------------------------+
+---------+--------------+----------------+--------+---------+----------+
|Variable | Coefficient | Standard Error |t-ratio |P[|T|>t] | Mean of X|
+---------+--------------+----------------+--------+---------+----------+
Constant -1.715878829 .11028619 -15.558 .0000
COMPULSO .4044068919 .13334650 3.033 .0037 .28125000
ARRIVAL -.9215830738E-01 .16395104 -.562 .5764 .93750000E-01
CONCURR .6642769614 .11313710 5.871 .0000 .26562500
SEATSHAR .4344681341E-04 .13203706E-01 .003 .9974 8.2995483
RESTDAY .6341938320 .12551583 5.053 .0000 .68750000
LIST -.1008869411 .16114748 -.626 .5339 .43750000
CONSTITU .1429460369 .13626549 1.049 .2988 .31250000
NETPAYER -.3374411246 .12302267 -2.743 .0082 .18750000
NETRECEI .3161625952 .14621074 2.162 .0350 .28125000
--> REGRESS;Lhs=LNDIFF;Rhs=ONE, compulso, arrival, concurr, seatshar, restday...
,constitu, netpayer, netrecei, y1999, sweden, italcomp, france$
+-----------------------------------------------------------------------+
| Ordinary least squares regression Weighting variable = none |
| Dep. var. = LNDIFF Mean= -.9717786972 , S.D.= .6841621581 |
| Model size: Observations = 64, Parameters = 14, Deg.Fr.= 50 |
| Residuals: Sum of squares= 2.685506595 , Std.Dev.= .23175 |
| Fit: R-squared= .908932, Adjusted R-squared = .88525 |
| Model test: F[ 13, 50] = 38.39, Prob value = .00000 |
| Diagnostic: Log-L = 10.6604, Restricted(b=0) Log-L = -66.0163 |
| LogAmemiyaPrCrt.= -2.726, Akaike Info. Crt.= .104 |
| Autocorrel: Durbin-Watson Statistic = 1.96305, Rho = .01848 |
+-----------------------------------------------------------------------+
+---------+--------------+----------------+--------+---------+----------+
|Variable | Coefficient | Standard Error |t-ratio |P[|T|>t] | Mean of X|
+---------+--------------+----------------+--------+---------+----------+
Constant -1.780610638 .93332121E-01 -19.078 .0000
COMPULSO .4900456025 .12201058 4.016 .0002 .28125000
ARRIVAL .1517122057 .13154412 1.153 .2543 .93750000E-01
CONCURR .5251938122 .83718367E-01 6.273 .0000 .26562500
SEATSHAR .3609608081E-02 .14805397E-01 .244 .8084 8.2995483
RESTDAY .6549210674 .96025689E-01 6.820 .0000 .68750000
LIST -.3944938317 .15518835 -2.542 .0142 .43750000
CONSTITU .2794475956 .10896176 2.565 .0134 .31250000
NETPAYER .1588513974E-02 .10714972 .015 .9882 .18750000
NETRECEI .5460715874 .12226257 4.466 .0000 .28125000
Y1999 -.1293367237 .78219676E-01 -1.654 .1045 .23437500
SWEDEN -.9875988284 .21067254 -4.688 .0000 .31250000E-01
ITALCOMP -.5104663134 .23881740 -2.137 .0375 .46875000E-01
FRANCE .6854362028 .14206344 4.825 .0000 .78125000E-01
--> 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= 5.497892490 , Std.Dev.= .32208 |
| Fit: R-squared= .900991, Adjusted R-squared = .88231 |
| Model test: F[ 10, 53] = 48.23, Prob value = .00000 |
| Diagnostic: Log-L = -12.2675, Restricted(b=0) Log-L = -86.2690 |
| LogAmemiyaPrCrt.= -2.107, Akaike Info. Crt.= .727 |
| Autocorrel: Durbin-Watson Statistic = 1.40146, Rho = .29927 |
+-----------------------------------------------------------------------+
+---------+--------------+----------------+--------+---------+----------+
|Variable | Coefficient | Standard Error |t-ratio |P[|T|>t] | Mean of X|
+---------+--------------+----------------+--------+---------+----------+
Constant -1.359793995 .19525857 -6.964 .0000
COMPULSO .6122046760 .16043497 3.816 .0004 .28125000
ARRIVAL -.6857832702E-01 .15892193 -.432 .6678 .93750000E-01
CONCURR .6045309086 .11280075 5.359 .0000 .26562500
SEATSHAR .5022336633E-02 .12972106E-01 .387 .7002 8.2995483
RESTDAY .6356477600 .12138467 5.237 .0000 .68750000
LIST -.2182967810 .16490835 -1.324 .1913 .43750000
CONSTITU .1306807603 .13189889 .991 .3263 .31250000
NETPAYER -.2922850012 .12076619 -2.420 .0190 .18750000
NETRECEI .2537381409 .14427417 1.759 .0844 .28125000
LNNP .7466007546 .11639098 6.415 .0000 1.4952722
--> REGRESS;Lhs=lnep;Rhs=ONE, compulso, arrival, concurr, seatshar, restday, ...
,constitu, netpayer, netrecei, lnnp, y1999, italcomp,uk, luxembou, sweden
$
+-----------------------------------------------------------------------+
| Ordinary least squares regression Weighting variable = none |
| Dep. var. = LNEP Mean= .5234934857 , S.D.= .9388390734 |
| Model size: Observations = 64, Parameters = 16, Deg.Fr.= 48 |
| Residuals: Sum of squares= 2.020048105 , Std.Dev.= .20514 |
| Fit: R-squared= .963622, Adjusted R-squared = .95225 |
| Model test: F[ 15, 48] = 84.77, Prob value = .00000 |
| Diagnostic: Log-L = 19.7723, Restricted(b=0) Log-L = -86.2690 |
| LogAmemiyaPrCrt.= -2.945, Akaike Info. Crt.= -.118 |
| Autocorrel: Durbin-Watson Statistic = 2.03481, Rho = -.01740 |
+-----------------------------------------------------------------------+
+---------+--------------+----------------+--------+---------+----------+
|Variable | Coefficient | Standard Error |t-ratio |P[|T|>t] | Mean of X|
+---------+--------------+----------------+--------+---------+----------+
Constant -1.098761439 .15118105 -7.268 .0000
COMPULSO .7342851113 .13722281 5.351 .0000 .28125000
ARRIVAL .2600033269 .12913409 2.013 .0497 .93750000E-01
CONCURR .3857261057 .86647526E-01 4.452 .0001 .26562500
SEATSHAR .1408316616E-01 .14183266E-01 .993 .3257 8.2995483
RESTDAY .3803437397 .12093550 3.145 .0028 .68750000
LIST .7729731584E-01 .23361384 .331 .7422 .43750000
CONSTITU .5903790503 .14958587 3.947 .0003 .31250000
NETPAYER -.2782095054 .90363656E-01 -3.079 .0034 .18750000
NETRECEI .3234576870E-02 .15331452 .021 .9833 .28125000
LNNP .5881444408 .82296688E-01 7.147 .0000 1.4952722
Y1999 -.1854150273 .73003454E-01 -2.540 .0144 .23437500
ITALCOMP -.6993530377 .22283279 -3.138 .0029 .46875000E-01
UK -1.151199970 .27578130 -4.174 .0001 .78125000E-01
LUXEMBOU .5812383689 .19121835 3.040 .0038 .78125000E-01
SWEDEN -.4861514350 .19722959 -2.465 .0173 .31250000E-01