gretl version 2016d-git Copyright Ramu Ramanathan, Allin Cottrell and Riccardo "Jack" Lucchetti This is free software with ABSOLUTELY NO WARRANTY Current session: 2016-09-04 09:48 ? run Rsim.inp /home/cottrell/stats/midas/Rsim.inp Call: lm(formula = y ~ trend + mls(x, 0:7, 4) + mls(z, 0:16, 12)) Residuals: Min 1Q Median 3Q Max -2.2651 -0.6489 0.1073 0.6780 2.7707 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.9694327 0.1261210 15.615 < 2e-16 *** trend 0.1000072 0.0008769 114.047 < 2e-16 *** mls(x, 0:7, 4)X.0/m 0.5268124 0.0643322 8.189 2.07e-14 *** mls(x, 0:7, 4)X.1/m 0.3782006 0.0641497 5.896 1.38e-08 *** mls(x, 0:7, 4)X.2/m 0.1879689 0.0680465 2.762 0.006219 ** mls(x, 0:7, 4)X.3/m -0.0052409 0.0658730 -0.080 0.936658 mls(x, 0:7, 4)X.4/m 0.1504419 0.0627623 2.397 0.017358 * mls(x, 0:7, 4)X.5/m 0.0104345 0.0655386 0.159 0.873647 mls(x, 0:7, 4)X.6/m 0.0698753 0.0692803 1.009 0.314270 mls(x, 0:7, 4)X.7/m 0.1463317 0.0650285 2.250 0.025412 * mls(z, 0:16, 12)X.0/m 0.1926168 0.0618960 3.112 0.002103 ** mls(z, 0:16, 12)X.1/m 0.1371191 0.0599615 2.287 0.023151 * mls(z, 0:16, 12)X.2/m 0.2383446 0.0659546 3.614 0.000373 *** mls(z, 0:16, 12)X.3/m 0.1988860 0.0577702 3.443 0.000688 *** mls(z, 0:16, 12)X.4/m 0.2440035 0.0700954 3.481 0.000601 *** mls(z, 0:16, 12)X.5/m 0.1513840 0.0632036 2.395 0.017443 * mls(z, 0:16, 12)X.6/m 0.0359735 0.0630194 0.571 0.568691 mls(z, 0:16, 12)X.7/m 0.0559475 0.0673284 0.831 0.406887 mls(z, 0:16, 12)X.8/m -0.0046812 0.0624569 -0.075 0.940321 mls(z, 0:16, 12)X.9/m 0.0458605 0.0675116 0.679 0.497656 mls(z, 0:16, 12)X.10/m 0.0383291 0.0664136 0.577 0.564438 mls(z, 0:16, 12)X.11/m -0.0077985 0.0591409 -0.132 0.895212 mls(z, 0:16, 12)X.12/m -0.0283257 0.0620632 -0.456 0.648548 mls(z, 0:16, 12)X.13/m -0.0375066 0.0608348 -0.617 0.538175 mls(z, 0:16, 12)X.14/m 0.0297271 0.0652273 0.456 0.649018 mls(z, 0:16, 12)X.15/m 0.0184075 0.0588059 0.313 0.754558 mls(z, 0:16, 12)X.16/m -0.0546460 0.0677214 -0.807 0.420574 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.9383 on 222 degrees of freedom (1 observation deleted due to missingness) Multiple R-squared: 0.9853, Adjusted R-squared: 0.9836 F-statistic: 573.4 on 26 and 222 DF, p-value: < 2.2e-16 Formula y ~ trend + mls(x, 0:7, 4, nealmon) + mls(z, 0:16, 12, nealmon) Parameters: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.9873850 0.1151350 17.261 < 2e-16 *** trend 0.0998809 0.0007765 128.637 < 2e-16 *** x1 1.3546712 0.1506701 8.991 < 2e-16 *** x2 -0.5079702 0.0964508 -5.267 3.07e-07 *** z1 1.2592097 0.1784000 7.058 1.77e-11 *** z2 0.4469820 0.2899442 1.542 0.1245 z3 -0.0678933 0.0346751 -1.958 0.0514 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.9322 on 242 degrees of freedom n = 249 SSR = 210.3011 wrote matrix /home/cottrell/.gretl/Rx.mat wrote matrix /home/cottrell/.gretl/Rz.mat wrote matrix /home/cottrell/.gretl/Ru.mat wrote matrix /home/cottrell/.gretl/Ry.mat Warning: generated non-finite values OLS, using observations 2-250 (n = 249) Dependent variable: y coefficient std. error t-ratio p-value ------------------------------------------------------- const 0.00000 0.00000 NA NA ry 1.00000 0.00000 NA NA SSR = 0, R-squared = 1.000000 Model 2: MIDAS (OLS), using observations 2-250 (n = 249) Dependent variable: y estimate std. error t-ratio p-value ------------------------------------------------------------ const 1.96943 0.126121 15.62 1.37e-37 *** trend 0.100007 0.000876891 114.0 4.88e-199 *** x4_0 0.526812 0.0643322 8.189 2.07e-14 *** x3_0 0.378201 0.0641497 5.896 1.38e-08 *** x2_0 0.187969 0.0680465 2.762 0.0062 *** x1_0 -0.00524094 0.0658730 -0.07956 0.9367 x4_1 0.150442 0.0627623 2.397 0.0174 ** x3_1 0.0104345 0.0655386 0.1592 0.8736 x2_1 0.0698753 0.0692803 1.009 0.3143 x1_1 0.146332 0.0650285 2.250 0.0254 ** z12_0 0.192617 0.0618960 3.112 0.0021 *** z11_0 0.137119 0.0599615 2.287 0.0232 ** z10_0 0.238345 0.0659546 3.614 0.0004 *** z9_0 0.198886 0.0577702 3.443 0.0007 *** z8_0 0.244003 0.0700954 3.481 0.0006 *** z7_0 0.151384 0.0632036 2.395 0.0174 ** z6_0 0.0359735 0.0630194 0.5708 0.5687 z5_0 0.0559475 0.0673284 0.8310 0.4069 z4_0 -0.00468121 0.0624569 -0.07495 0.9403 z3_0 0.0458605 0.0675116 0.6793 0.4977 z2_0 0.0383291 0.0664136 0.5771 0.5644 z1_0 -0.00779848 0.0591409 -0.1319 0.8952 z12_1 -0.0283257 0.0620632 -0.4564 0.6485 z11_1 -0.0375066 0.0608348 -0.6165 0.5382 z10_1 0.0297271 0.0652273 0.4557 0.6490 z9_1 0.0184075 0.0588059 0.3130 0.7546 z8_1 -0.0546460 0.0677214 -0.8069 0.4206 Mean dependent var 14.60326 S.D. dependent var 7.328439 Sum squared resid 195.4368 S.E. of regression 0.938268 R-squared 0.985327 Adjusted R-squared 0.983608 F(26, 222) 573.3608 P-value(F) 3.7e-188 Log-likelihood -323.1599 Akaike criterion 700.3197 Schwarz criterion 795.2909 Hannan-Quinn 738.5472 Convergence achieved after 10 iterations Model 3: MIDAS (NLS), using observations 2-250 (n = 249) Dependent variable: y estimate std. error t-ratio p-value ---------------------------------------------------------- const 1.98735 0.119910 16.57 9.69e-42 *** trend 0.0998810 0.000827330 120.7 3.11e-218 *** HF_slope1 1.35545 0.164572 8.236 1.12e-14 *** Almon1 -0.507191 0.0933403 -5.434 1.34e-07 *** HF_slope2 1.25900 0.192594 6.537 3.69e-10 *** Almon1 0.447270 0.283951 1.575 0.1165 Almon2 -0.0679537 0.0339579 -2.001 0.0465 ** Mean dependent var 14.60326 S.D. dependent var 7.328439 Sum squared resid 210.3010 S.E. of regression 0.932208 R-squared 0.984211 Adjusted R-squared 0.983819 Log-likelihood -332.2860 Akaike criterion 678.5721 Schwarz criterion 703.1942 Hannan-Quinn 688.4829 GNR: R-squared = 4.88498e-15, max |t| = 6.29419e-07 Convergence seems to be reasonably complete