MODEL COMMAND: REGRESS;LHS=SPEED;RHS=ONE,TIME$ Ordinary least squares regression. Dep. Variable = SPEED Observations = 81 Weights = ONE Mean of LHS = 0.1292524E+03 Std.Dev of LHS = 0.2795229E+02 StdDev of residuals= 0.9139178E+01 Sum of squares = 0.6598442E+04 R-squared = 0.8944358E+00 Adjusted R-squared= 0.8930995E+00 F[ 1, 79] = 0.6693598E+03 Log-likelihood = -0.2931397E+03 Restr.(á=0) Log-l = -0.3842013E+03 Amemiya Pr. Criter.= 0.7287399E+01 Akaike Info.Crit. = 0.8558691E+02 ANOVA Source Variation Degrees of Freedom Mean Square Regression 0.5590799E+05 1. 0.5590799E+05 Residual 0.6598442E+04 79. 0.8352458E+02 Total 0.6250643E+05 80. 0.7813304E+03 Durbin-Watson stat.= 0.8929121 Autocorrelation = 0.5535439 Variable Coefficient Std. Error t-ratio Prob|t|òx Mean of X Std.Dev.of X ------------------------------------------------------------------------------- Constant 81.972 2.091 39.209 0.00000 TIME 1.0410 0.4024E-01 25.872 0.00000 45.420 25.395 MODEL COMMAND: REGRESS;LHS=SPEED;RHS=ONE,TIME,TIME2$ Ordinary least squares regression. Dep. Variable = SPEED Observations = 81 Weights = ONE Mean of LHS = 0.1292524E+03 Std.Dev of LHS = 0.2795229E+02 StdDev of residuals= 0.7921152E+01 Sum of squares = 0.4894082E+04 R-squared = 0.9217027E+00 Adjusted R-squared= 0.9196951E+00 F[ 2, 78] = 0.4591018E+03 Log-likelihood = -0.2810380E+03 Restr.(á=0) Log-l = -0.3842013E+03 Amemiya Pr. Criter.= 0.7013284E+01 Akaike Info.Crit. = 0.6506852E+02 ANOVA Source Variation Degrees of Freedom Mean Square Regression 0.5761235E+05 2. 0.2880618E+05 Residual 0.4894082E+04 78. 0.6274464E+02 Total 0.6250643E+05 80. 0.7813304E+03 Durbin-Watson stat.= 1.1983880 Autocorrelation = 0.4008060 Variable Coefficient Std. Error t-ratio Prob|t|òx Mean of X Std.Dev.of X ------------------------------------------------------------------------------- Constant 71.179 2.752 25.868 0.00000 TIME 1.7638 0.1430 12.334 0.00000 45.420 25.395 TIME2 -0.81623E-02 0.1566E-02 -5.212 0.00000 2699.9 2318.9 MODEL COMMAND: REGRESS;LHS=SPEED;RHS=ONE,TIME,TIME2,TIME3$ Ordinary least squares regression. Dep. Variable = SPEED Observations = 81 Weights = ONE Mean of LHS = 0.1292524E+03 Std.Dev of LHS = 0.2795229E+02 StdDev of residuals= 0.7184774E+01 Sum of squares = 0.3974815E+04 R-squared = 0.9364095E+00 Adjusted R-squared= 0.9339319E+00 F[ 3, 77] = 0.3779576E+03 Log-likelihood = -0.2726120E+03 Restr.(á=0) Log-l = -0.3842013E+03 Amemiya Pr. Criter.= 0.6829927E+01 Akaike Info.Crit. = 0.5417016E+02 ANOVA Source Variation Degrees of Freedom Mean Square Regression 0.5853162E+05 3. 0.1951054E+05 Residual 0.3974815E+04 77. 0.5162098E+02 Total 0.6250643E+05 80. 0.7813304E+03 Durbin-Watson stat.= 1.4724048 Autocorrelation = 0.2637976 Variable Coefficient Std. Error t-ratio Prob|t|òx Mean of X Std.Dev.of X ------------------------------------------------------------------------------- Constant 80.982 3.409 23.752 0.00000 TIME 0.48224 0.3302 1.460 0.14827 45.420 25.395 TIME2 0.27519E-01 0.8574E-02 3.210 0.00194 2699.9 2318.9 TIME3 -0.26772E-03 0.6344E-04 -4.220 0.00007 0.17904E+06 0.19386E+06 MODEL COMMAND: REGRESS;LHS=SPEED;RHS=ONE,INTERWAR,POSTWAR,TIME$ Ordinary least squares regression. Dep. Variable = SPEED Observations = 81 Weights = ONE Mean of LHS = 0.1292524E+03 Std.Dev of LHS = 0.2795229E+02 StdDev of residuals= 0.8747400E+01 Sum of squares = 0.5891810E+04 R-squared = 0.9057407E+00 Adjusted R-squared= 0.9020683E+00 F[ 3, 77] = 0.2466320E+03 Log-likelihood = -0.2885522E+03 Restr.(á=0) Log-l = -0.3842013E+03 Amemiya Pr. Criter.= 0.7223512E+01 Akaike Info.Crit. = 0.8029563E+02 ANOVA Source Variation Degrees of Freedom Mean Square Regression 0.5661462E+05 3. 0.1887154E+05 Residual 0.5891810E+04 77. 0.7651702E+02 Total 0.6250643E+05 80. 0.7813304E+03 Durbin-Watson stat.= 1.0360607 Autocorrelation = 0.4819697 Variable Coefficient Std. Error t-ratio Prob|t|òx Mean of X Std.Dev.of X ------------------------------------------------------------------------------- Constant 77.979 3.581 21.773 0.00000 INTERWAR 7.0278 4.209 1.670 0.09903 0.28395 0.45372 POSTWAR 17.207 5.867 2.933 0.00442 0.64198 0.48241 TIME 0.84173 0.7749E-01 10.862 0.00000 45.420 25.395 MODEL COMMAND: REGRESS;LHS=SPEED;RHS=ONE,TIME,INTERTIM,POSTTIME$ Ordinary least squares regression. Dep. Variable = SPEED Observations = 81 Weights = ONE Mean of LHS = 0.1292524E+03 Std.Dev of LHS = 0.2795229E+02 StdDev of residuals= 0.9158024E+01 Sum of squares = 0.6457944E+04 R-squared = 0.8966835E+00 Adjusted R-squared= 0.8926582E+00 F[ 3, 77] = 0.2227610E+03 Log-likelihood = -0.2922680E+03 Restr.(á=0) Log-l = -0.3842013E+03 Amemiya Pr. Criter.= 0.7315260E+01 Akaike Info.Crit. = 0.8801110E+02 ANOVA Source Variation Degrees of Freedom Mean Square Regression 0.5604849E+05 3. 0.1868283E+05 Residual 0.6457944E+04 77. 0.8386940E+02 Total 0.6250643E+05 80. 0.7813304E+03 Durbin-Watson stat.= 0.9323582 Autocorrelation = 0.5338209 Variable Coefficient Std. Error t-ratio Prob|t|òx Mean of X Std.Dev.of X ------------------------------------------------------------------------------- Constant 85.447 3.633 23.522 0.00000 TIME -0.56572 1.275 -0.444 0.65838 45.420 25.395 INTERTIM 1.4294 1.177 1.215 0.22825 5.6790 9.7466 POSTTIME 1.5547 1.239 1.255 0.21323 39.481 32.041 MODEL COMMAND: REGREGRESS;LHS=SPEED;RHS=ONE,INTERWAR,INTERTIM,POSTWAR,POSTT IME,TIME$ Ordinary least squares regression. Dep. Variable = SPEED Observations = 81 Weights = ONE Mean of LHS = 0.1292524E+03 Std.Dev of LHS = 0.2795229E+02 StdDev of residuals= 0.8677561E+01 Sum of squares = 0.5647505E+04 R-squared = 0.9096492E+00 Adjusted R-squared= 0.9036258E+00 F[ 5, 75] = 0.1510196E+03 Log-likelihood = -0.2868371E+03 Restr.(á=0) Log-l = -0.3842013E+03 Amemiya Pr. Criter.= 0.7230545E+01 Akaike Info.Crit. = 0.8087785E+02 ANOVA Source Variation Degrees of Freedom Mean Square Regression 0.5685893E+05 5. 0.1137179E+05 Residual 0.5647505E+04 75. 0.7530007E+02 Total 0.6250643E+05 80. 0.7813304E+03 Durbin-Watson stat.= 1.0594093 Autocorrelation = 0.4702954 Variable Coefficient Std. Error t-ratio Prob|t|òx Mean of X Std.Dev.of X ------------------------------------------------------------------------------- Constant 72.230 8.078 8.941 0.00000 INTERWAR 4.3554 9.914 0.439 0.66171 0.28395 0.45372 INTERTIM -1.2215 2.092 -0.584 0.56109 5.6790 9.7466 POSTWAR 25.345 9.541 2.657 0.00964 0.64198 0.48241 POSTTIME -1.6814 2.076 -0.810 0.42052 39.481 32.041 TIME 2.4843 2.074 1.198 0.23483 45.420 25.395 MODEL COMMAND: REGRESS;LHS=SPEED;RHS=ONE,TIME,WAR1,WAR1TIME,POSTWAR,POSTTIM E$ Ordinary least squares regression. Dep. Variable = SPEED Observations = 81 Weights = ONE Mean of LHS = 0.1292524E+03 Std.Dev of LHS = 0.2795229E+02 StdDev of residuals= 0.8677561E+01 Sum of squares = 0.5647505E+04 R-squared = 0.9096492E+00 Adjusted R-squared= 0.9036258E+00 F[ 5, 75] = 0.1510196E+03 Log-likelihood = -0.2868371E+03 Restr.(á=0) Log-l = -0.3842013E+03 Amemiya Pr. Criter.= 0.7230545E+01 Akaike Info.Crit. = 0.8087785E+02 ANOVA Source Variation Degrees of Freedom Mean Square Regression 0.5685893E+05 5. 0.1137179E+05 Residual 0.5647505E+04 75. 0.7530007E+02 Total 0.6250643E+05 80. 0.7813304E+03 Durbin-Watson stat.= 1.0594093 Autocorrelation = 0.4702954 Variable Coefficient Std. Error t-ratio Prob|t|òx Mean of X Std.Dev.of X ------------------------------------------------------------------------------- Constant 72.230 8.078 8.941 0.00000 TIME 2.4843 2.074 1.198 0.23483 45.420 25.395 WAR1 4.3554 9.914 0.439 0.66171 0.92593 0.26352 WAR1TIME -1.2215 2.092 -0.584 0.56109 45.160 25.837 POSTWAR 20.989 7.668 2.737 0.00773 0.64198 0.48241 POSTTIME -0.45990 0.2843 -1.618 0.10995 39.481 32.041 MODEL COMMAND: REGRESS;LHS=SPEED;RHS=ONE,TIME,WAR1,WAR1TIME,POSTWAR,POSTTIM E;CLS:B(3)+3B(2)+9B(4)=0,B(5)+5B(2)+5B(4)+36B(6)=0$ Ordinary least squares regression. Dep. Variable = SPEED Observations = 81 Weights = ONE Mean of LHS = 0.1292524E+03 Std.Dev of LHS = 0.2795229E+02 StdDev of residuals= 0.8677561E+01 Sum of squares = 0.5647505E+04 R-squared = 0.9096492E+00 Adjusted R-squared= 0.9036258E+00 F[ 5, 75] = 0.1510196E+03 Log-likelihood = -0.2868371E+03 Restr.(á=0) Log-l = -0.3842013E+03 Amemiya Pr. Criter.= 0.7230545E+01 Akaike Info.Crit. = 0.8087785E+02 ANOVA Source Variation Degrees of Freedom Mean Square Regression 0.5685893E+05 5. 0.1137179E+05 Residual 0.5647505E+04 75. 0.7530007E+02 Total 0.6250643E+05 80. 0.7813304E+03 Durbin-Watson stat.= 1.0594093 Autocorrelation = 0.4702954 Variable Coefficient Std. Error t-ratio Prob|t|òx Mean of X Std.Dev.of X ------------------------------------------------------------------------------- Constant 72.230 8.078 8.941 0.00000 TIME 2.4843 2.074 1.198 0.23483 45.420 25.395 WAR1 4.3554 9.914 0.439 0.66171 0.92593 0.26352 WAR1TIME -1.2215 2.092 -0.584 0.56109 45.160 25.837 POSTWAR 20.989 7.668 2.737 0.00773 0.64198 0.48241 POSTTIME -0.45990 0.2843 -1.618 0.10995 39.481 32.041 Linearly Restricted Regression Ordinary least squares regression. Dep. Variable = SPEED Observations = 81 Weights = ONE Mean of LHS = 0.1292524E+03 Std.Dev of LHS = 0.2795229E+02 StdDev of residuals= 0.8932358E+01 Sum of squares = 0.6143601E+04 R-squared = 0.9017125E+00 Adjusted R-squared= 0.8978831E+00 F[ 3, 77] = 0.2354720E+03 Log-likelihood = -0.2902471E+03 Restr.(á=0) Log-l = -0.3842013E+03 Amemiya Pr. Criter.= 0.7265360E+01 Akaike Info.Crit. = 0.8372712E+02 ANOVA Source Variation Degrees of Freedom Mean Square Regression 0.5636283E+05 3. 0.1878761E+05 Residual 0.6143601E+04 77. 0.7978702E+02 Total 0.6250643E+05 80. 0.7813304E+03 F[ 2, 75] for the restrictions = 3.2941 Prob = 0.0425 Variable Coefficient Std. Error t-ratio Prob|t|òx Mean of X Std.Dev.of X ------------------------------------------------------------------------------- Constant 73.550 7.468 9.849 0.00000 TIME 1.9185 1.450 1.323 0.18972 45.420 25.395 WAR1 -4.0120 9.593 -0.418 0.67698 0.92593 0.26352 WAR1TIME -0.19372 1.545 -0.125 0.90059 45.160 25.837 POSTWAR 20.922 7.374 2.837 0.00585 0.64198 0.48241 POSTTIME -0.82072 0.2290 -3.585 0.00060 39.481 32.041 MODEL COMMAND: BOXCOX;LHS=SPEED;RHS=ONE,TIME,MONEY1;MLE;LAMBDA=.4;MODEL=2$ ******************************************************************************* Method=D/F/P ; Maximum iterations= 50 Convergence criteria: Gradient= 0.1000000E-03 Function = 0.0000000 Parameters= 0.1000000E-03 Starting values: 8.021 0.9528 60.49 0.4000 79.22 ==> Steepest Descent Iters. Iteration: 1 Fn= 292.0082 Param 8.02 0.953 60.5 0.400 79.2 Gradnt -0.494E-11-0.674E-12-0.503E-12 -30.8 0.459E-12 Iteration: 37 Fn= 276.9895 Param 1.24 -2.02 75.2 1.01 54.7 Gradnt -0.865E-02-0.141E-02-0.959E-04-0.350E-01-0.613E-05 ** B-vector has converged. ******************************************************************************* Box-Cox nonlinear regression model Maximum likelihood estimates Weights = ONE Mean of LHS = 0.1292524E+03 Std.Dev of LHS = 0.2795229E+02 StdDev of residuals= 0.7394079E+01 Sum of squares = 0.4428464E+04 Trans.: RHS=Lambda , LHS=Lambda Elasticities kept in matrix EPSILON Log-likelihood = -0.27698946E+03 Variable Coefficient Std. Error t-ratio Prob|t|òx Mean of X Std.Dev.of X ------------------------------------------------------------------------------- TIME 1.2437 0.2053 6.057 0.00000 45.420 25.395 MONEY1 -2.0220 1.089 -1.856 0.06344 2.6374 3.8715 Constant 76.232 35.42 2.152 0.03139 Lambda 1.0065 0.1275 7.897 0.00000 åý 54.672 70.34 0.777 0.43703 End cmnd. entry from editor