R Under development (unstable) (2024-10-26 r87273 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(tsDyn) > suppressMessages(library(dplyr)) > library(tidyr) > > ###SETAR > > mod.setar <- setar(log10(lynx), m=2, thDelay=1, th=3.25) Warning message: Possible unit root in the high regime. Roots are: 0.8985 0.8985 > mod.setar Non linear autoregressive model SETAR model ( 2 regimes) Coefficients: Low regime: const.L phiL.1 phiL.2 0.5908673 1.2538064 -0.4184042 High regime: const.H phiH.1 phiH.2 2.232671 1.526853 -1.238662 Threshold: -Variable: Z(t) = + (0) X(t)+ (1)X(t-1) -Value: 3.25 (fixed) Proportion of points in low regime: 66.96% High regime: 33.04% > summary(mod.setar) Non linear autoregressive model SETAR model ( 2 regimes) Coefficients: Low regime: const.L phiL.1 phiL.2 0.5908673 1.2538064 -0.4184042 High regime: const.H phiH.1 phiH.2 2.232671 1.526853 -1.238662 Threshold: -Variable: Z(t) = + (0) X(t)+ (1)X(t-1) -Value: 3.25 (fixed) Proportion of points in low regime: 66.96% High regime: 33.04% Residuals: Min 1Q Median 3Q Max -0.5769141 -0.1198456 0.0034299 0.1191886 0.5173509 Fit: residuals variance = 0.04053, AIC = -353, MAPE = 5.76% Coefficient(s): Estimate Std. Error t value Pr(>|t|) const.L 0.590867 0.152011 3.8870 0.0001755 *** phiL.1 1.253806 0.071265 17.5936 < 2.2e-16 *** phiL.2 -0.418404 0.087630 -4.7746 5.690e-06 *** const.H 2.232671 0.801695 2.7849 0.0063238 ** phiH.1 1.526853 0.103082 14.8121 < 2.2e-16 *** phiH.2 -1.238662 0.255539 -4.8473 4.219e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Threshold Variable: Z(t) = + (0) X(t) + (1) X(t-1) Value: 3.25 (fixed) > > set0 <- setar(lynx,m=2) Warning message: Possible unit root in the low regime. Roots are: 0.4681 5.0788 > set1 <- setar(lynx,m=2, th=1426,trace=TRUE,thDelay=1) Using maximum autoregressive order for low regime: mL = 2 Using maximum autoregressive order for high regime: mH = 2 Warning message: Possible unit root in the low regime. Roots are: 0.8477 2.7487 > set2 <- setar(lynx,m=2, th=c(600,1426),trace=TRUE) Using maximum autoregressive order for low regime: mL = 2 Using maximum autoregressive order for high regime: mH = 2 Using maximum autoregressive order for middle regime: mM = 2 Warning messages: 1: Possible unit root in the low regime. Roots are: 0.506 5.4704 2: Possible unit root in the medium regime. Roots are: 0.508 3.9149 > set3 <- setar(lynx,m=2, trace=TRUE,nthresh=1) Using maximum autoregressive order for low regime: mL = 2 Using maximum autoregressive order for high regime: mH = 2 Searching on 74 possible threshold values within regimes with sufficient ( 15% ) number of observations Searching on 74 combinations of thresholds (74) and thDelay (1) Selected threshold: 1388 Selected delay: 0 Warning message: Possible unit root in the low regime. Roots are: 0.4681 5.0788 > set4 <- setar(lynx,m=2, trace=TRUE,nthresh=2, trim=0.05) Using maximum autoregressive order for low regime: mL = 2 Using maximum autoregressive order for high regime: mH = 2 Using maximum autoregressive order for middle regime: mM = 2 Searching on 96 possible threshold values within regimes with sufficient ( 5% ) number of observations Searching on 96 combinations of thresholds (96) and thDelay (1) Result of the one threshold search: -Thresh: 1388 -Delay: 0 - SSR 67408458 Second best: 3465 (conditionnal on th= 1388 and Delay= 0 ) SSR/AIC: 53987715 Second best: 1388 (conditionnal on th= 3465 and Delay= 0 ) SSR/AIC: 53987715 Selected threshold: 1388 3465 Selected delay: 0 Warning message: Possible unit root in the low regime. Roots are: 0.4681 5.0788 > set5 <- setar(lynx,m=2, th=c(600,1426),include="trend") Warning messages: 1: Possible unit root in the low regime. Roots are: 0.4933 5.5739 2: Possible unit root in the medium regime. Roots are: 0.5329 3.7413 > set6 <- setar(lynx,m=2, th=c(600,1426),include="both") Warning messages: 1: Possible unit root in the low regime. Roots are: 0.5044 5.4712 2: Possible unit root in the medium regime. Roots are: 0.5168 3.9008 > set7 <- setar(lynx,m=2, th=c(600,1426),include="none") Warning messages: 1: Possible unit root in the low regime. Roots are: 0.4791 5.7623 2: Possible unit root in the medium regime. Roots are: 0.4929 4.0747 > > set_all <- list(set0=set0, set1=set1, set2=set2, set3=set3, set4=set4, + set5=set5, set6=set6, set7=set7) > > set_all $set0 Non linear autoregressive model SETAR model ( 2 regimes) Coefficients: Low regime: const.L phiL.1 phiL.2 -2.4440208 2.3330905 -0.4206083 High regime: const.H phiH.1 phiH.2 1465.133661 1.044104 -0.741993 Threshold: -Variable: Z(t) = + (1) X(t)+ (0)X(t-1) -Value: 1388 Proportion of points in low regime: 58.93% High regime: 41.07% $set1 Non linear autoregressive model SETAR model ( 2 regimes) Coefficients: Low regime: const.L phiL.1 phiL.2 315.6983921 1.5434194 -0.4291513 High regime: const.H phiH.1 phiH.2 1292.8489487 0.9830795 -0.6362265 Threshold: -Variable: Z(t) = + (0) X(t)+ (1)X(t-1) -Value: 1426 (fixed) Proportion of points in low regime: 60.71% High regime: 39.29% $set2 Non linear autoregressive model SETAR model ( 3 regimes) Coefficients: Low regime: const.L phiL.1 phiL.2 37.7065835 2.1592162 -0.3612925 Mid regime: const.M phiM.1 phiM.2 55.9687980 2.2239335 -0.5028274 High regime: const.H phiH.1 phiH.2 1507.797242 1.030366 -0.737548 Threshold: -Variable: Z(t) = + (1) X(t)+ (0)X(t-1)+ (1)X(t-0) -Value: 600 1426 (fixed) Proportion of points in low regime: 42.86% Middle regime: 16.96% High regime: 40.18% $set3 Non linear autoregressive model SETAR model ( 2 regimes) Coefficients: Low regime: const.L phiL.1 phiL.2 -2.4440208 2.3330905 -0.4206083 High regime: const.H phiH.1 phiH.2 1465.133661 1.044104 -0.741993 Threshold: -Variable: Z(t) = + (1) X(t)+ (0)X(t-1) -Value: 1388 Proportion of points in low regime: 58.93% High regime: 41.07% $set4 Non linear autoregressive model SETAR model ( 3 regimes) Coefficients: Low regime: const.L phiL.1 phiL.2 -2.4440208 2.3330905 -0.4206083 Mid regime: const.M phiM.1 phiM.2 48.082499 1.878627 -0.942074 High regime: const.H phiH.1 phiH.2 -60.0542362 1.0524925 -0.4482403 Threshold: -Variable: Z(t) = + (1) X(t)+ (0)X(t-1)+ (1)X(t-0) -Value: 1388 3465 Proportion of points in low regime: 58.93% Middle regime: 28.57% High regime: 12.5% $set5 Non linear autoregressive model SETAR model ( 3 regimes) Coefficients: Low regime: trend.L phiL.1 phiL.2 0.4213327 2.2065858 -0.3636897 Mid regime: trend.M phiM.1 phiM.2 2.0483498 2.1436900 -0.5015428 High regime: trend.H phiH.1 phiH.2 7.8172099 1.2787370 -0.6923989 Threshold: -Variable: Z(t) = + (1) X(t)+ (0)X(t-1)+ (1)X(t-0) -Value: 600 1426 (fixed) Proportion of points in low regime: 42.86% Middle regime: 16.96% High regime: 40.18% $set6 Non linear autoregressive model SETAR model ( 3 regimes) Coefficients: Low regime: const.L trend.L phiL.1 phiL.2 24.5729061 0.2258515 2.1655117 -0.3623979 Mid regime: const.M trend.M phiM.1 phiM.2 -66.8766900 2.2453966 2.1913128 -0.4960353 High regime: const.H trend.H phiH.1 phiH.2 1509.18429228 -0.02568549 1.03036944 -0.73752250 Threshold: -Variable: Z(t) = + (1) X(t)+ (0)X(t-1)+ (1)X(t-0) -Value: 600 1426 (fixed) Proportion of points in low regime: 42.86% Middle regime: 16.96% High regime: 40.18% $set7 Non linear autoregressive model SETAR model ( 3 regimes) Coefficients: Low regime: phiL.1 phiL.2 2.2609406 -0.3622496 Mid regime: phiM.1 phiM.2 2.2743042 -0.4979271 High regime: phiH.1 phiH.2 1.3766159 -0.6640694 Threshold: -Variable: Z(t) = + (1) X(t)+ (0)X(t-1)+ (1)X(t-0) -Value: 600 1426 (fixed) Proportion of points in low regime: 42.86% Middle regime: 16.96% High regime: 40.18% > sapply(set_all, function(x) c(AIC=AIC(x), BIC=BIC(x), deviance=deviance(x))) set0 set1 set2 set3 set4 AIC 1529.069 1536.758 1535.870 1529.069 1511.760 BIC 1548.223 1553.175 1560.495 1548.223 1541.858 deviance 67408457.868 73387794.089 69084780.571 67408457.868 53987715.118 set5 set6 set7 AIC 1553.182 1541.727 1553.416 BIC 1577.807 1574.561 1569.833 deviance 80414426.423 68998167.617 84934338.081 > lapply(set_all, summary) $set0 Non linear autoregressive model SETAR model ( 2 regimes) Coefficients: Low regime: const.L phiL.1 phiL.2 -2.4440208 2.3330905 -0.4206083 High regime: const.H phiH.1 phiH.2 1465.133661 1.044104 -0.741993 Threshold: -Variable: Z(t) = + (1) X(t)+ (0)X(t-1) -Value: 1388 Proportion of points in low regime: 58.93% High regime: 41.07% Residuals: Min 1Q Median 3Q Max -2533.437 -323.543 -36.164 210.631 3003.094 Fit: residuals variance = 591302, AIC = 1529, MAPE = 57.57% Coefficient(s): Estimate Std. Error t value Pr(>|t|) const.L -2.444021 170.387877 -0.0143 0.9885821 phiL.1 2.333090 0.308262 7.5685 1.342e-11 *** phiL.2 -0.420608 0.110718 -3.7989 0.0002406 *** const.H 1465.133661 292.918295 5.0019 2.213e-06 *** phiH.1 1.044104 0.102162 10.2201 < 2.2e-16 *** phiH.2 -0.741993 0.085797 -8.6483 5.393e-14 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Threshold Variable: Z(t) = + (1) X(t) + (0) X(t-1) Value: 1388 $set1 Non linear autoregressive model SETAR model ( 2 regimes) Coefficients: Low regime: const.L phiL.1 phiL.2 315.6983921 1.5434194 -0.4291513 High regime: const.H phiH.1 phiH.2 1292.8489487 0.9830795 -0.6362265 Threshold: -Variable: Z(t) = + (0) X(t)+ (1)X(t-1) -Value: 1426 (fixed) Proportion of points in low regime: 60.71% High regime: 39.29% Residuals: Min 1Q Median 3Q Max -2487.76 -316.22 -109.01 318.48 3205.77 Fit: residuals variance = 643753, AIC = 1537, MAPE = 90.3% Coefficient(s): Estimate Std. Error t value Pr(>|t|) const.L 315.698392 168.539138 1.8731 0.0637521 . phiL.1 1.543419 0.179481 8.5994 6.946e-14 *** phiL.2 -0.429151 0.429384 -0.9995 0.3198068 const.H 1292.848949 320.827540 4.0297 0.0001042 *** phiH.1 0.983080 0.081078 12.1251 < 2.2e-16 *** phiH.2 -0.636226 0.100881 -6.3067 6.391e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Threshold Variable: Z(t) = + (0) X(t) + (1) X(t-1) Value: 1426 (fixed) $set2 Non linear autoregressive model SETAR model ( 3 regimes) Coefficients: Low regime: const.L phiL.1 phiL.2 37.7065835 2.1592162 -0.3612925 Mid regime: const.M phiM.1 phiM.2 55.9687980 2.2239335 -0.5028274 High regime: const.H phiH.1 phiH.2 1507.797242 1.030366 -0.737548 Threshold: -Variable: Z(t) = + (1) X(t)+ (0)X(t-1)+ (1)X(t-0) -Value: 600 1426 (fixed) Proportion of points in low regime: 42.86% Middle regime: 16.96% High regime: 40.18% Residuals: Min 1Q Median 3Q Max -2537.305 -261.684 -36.323 197.926 2998.700 Fit: residuals variance = 606007, AIC = 1536, MAPE = 60.66% Coefficient(s): Estimate Std. Error t value Pr(>|t|) const.L 37.706583 240.232605 0.1570 0.875579 phiL.1 2.159216 0.817575 2.6410 0.009528 ** phiL.2 -0.361293 0.180146 -2.0056 0.047477 * const.M 55.968798 782.900043 0.0715 0.943145 phiM.1 2.223933 0.749353 2.9678 0.003715 ** phiM.2 -0.502827 0.161151 -3.1202 0.002334 ** const.H 1507.797242 307.396717 4.9051 3.426e-06 *** phiH.1 1.030366 0.106872 9.6411 3.918e-16 *** phiH.2 -0.737548 0.088338 -8.3492 2.984e-13 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Threshold Variable: Z(t) = + (1) X(t) + (0) X(t-1) Value: 600 1426 (fixed) $set3 Non linear autoregressive model SETAR model ( 2 regimes) Coefficients: Low regime: const.L phiL.1 phiL.2 -2.4440208 2.3330905 -0.4206083 High regime: const.H phiH.1 phiH.2 1465.133661 1.044104 -0.741993 Threshold: -Variable: Z(t) = + (1) X(t)+ (0)X(t-1) -Value: 1388 Proportion of points in low regime: 58.93% High regime: 41.07% Residuals: Min 1Q Median 3Q Max -2533.437 -323.543 -36.164 210.631 3003.094 Fit: residuals variance = 591302, AIC = 1529, MAPE = 57.57% Coefficient(s): Estimate Std. Error t value Pr(>|t|) const.L -2.444021 170.387877 -0.0143 0.9885821 phiL.1 2.333090 0.308262 7.5685 1.342e-11 *** phiL.2 -0.420608 0.110718 -3.7989 0.0002406 *** const.H 1465.133661 292.918295 5.0019 2.213e-06 *** phiH.1 1.044104 0.102162 10.2201 < 2.2e-16 *** phiH.2 -0.741993 0.085797 -8.6483 5.393e-14 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Threshold Variable: Z(t) = + (1) X(t) + (0) X(t-1) Value: 1388 $set4 Non linear autoregressive model SETAR model ( 3 regimes) Coefficients: Low regime: const.L phiL.1 phiL.2 -2.4440208 2.3330905 -0.4206083 Mid regime: const.M phiM.1 phiM.2 48.082499 1.878627 -0.942074 High regime: const.H phiH.1 phiH.2 -60.0542362 1.0524925 -0.4482403 Threshold: -Variable: Z(t) = + (1) X(t)+ (0)X(t-1)+ (1)X(t-0) -Value: 1388 3465 Proportion of points in low regime: 58.93% Middle regime: 28.57% High regime: 12.5% Residuals: Min 1Q Median 3Q Max -2098.854 -248.316 -20.981 297.686 3133.350 Fit: residuals variance = 473576, AIC = 1512, MAPE = 55.94% Coefficient(s): Estimate Std. Error t value Pr(>|t|) const.L -2.44402 154.64867 -0.0158 0.9874210 phiL.1 2.33309 0.27979 8.3388 3.146e-13 *** phiL.2 -0.42061 0.10049 -4.1855 5.927e-05 *** const.M 48.08250 522.60383 0.0920 0.9268689 phiM.1 1.87863 0.21895 8.5803 9.184e-14 *** phiM.2 -0.94207 0.11164 -8.4386 1.893e-13 *** const.H -60.05424 831.14056 -0.0723 0.9425363 phiH.1 1.05249 0.16398 6.4185 4.061e-09 *** phiH.2 -0.44824 0.11390 -3.9354 0.0001495 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Threshold Variable: Z(t) = + (1) X(t) + (0) X(t-1) Value: 1388 3465 $set5 Non linear autoregressive model SETAR model ( 3 regimes) Coefficients: Low regime: trend.L phiL.1 phiL.2 0.4213327 2.2065858 -0.3636897 Mid regime: trend.M phiM.1 phiM.2 2.0483498 2.1436900 -0.5015428 High regime: trend.H phiH.1 phiH.2 7.8172099 1.2787370 -0.6923989 Threshold: -Variable: Z(t) = + (1) X(t)+ (0)X(t-1)+ (1)X(t-0) -Value: 600 1426 (fixed) Proportion of points in low regime: 42.86% Middle regime: 16.96% High regime: 40.18% Residuals: Min 1Q Median 3Q Max -2848.237 -227.684 -11.999 293.292 3266.908 Fit: residuals variance = 705390, AIC = 1553, MAPE = 57.92% Coefficient(s): Estimate Std. Error t value Pr(>|t|) trend.L 0.421333 3.031449 0.1390 0.889726 phiL.1 2.206586 0.664890 3.3187 0.001244 ** phiL.2 -0.363690 0.194522 -1.8697 0.064318 . trend.M 2.048350 6.198638 0.3305 0.741717 phiM.1 2.143690 0.481644 4.4508 2.138e-05 *** phiM.2 -0.501543 0.157732 -3.1797 0.001938 ** trend.H 7.817210 3.253418 2.4028 0.018026 * phiH.1 1.278737 0.095676 13.3653 < 2.2e-16 *** phiH.2 -0.692399 0.094663 -7.3143 5.364e-11 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Threshold Variable: Z(t) = + (1) X(t) + (0) X(t-1) Value: 600 1426 (fixed) $set6 Non linear autoregressive model SETAR model ( 3 regimes) Coefficients: Low regime: const.L trend.L phiL.1 phiL.2 24.5729061 0.2258515 2.1655117 -0.3623979 Mid regime: const.M trend.M phiM.1 phiM.2 -66.8766900 2.2453966 2.1913128 -0.4960353 High regime: const.H trend.H phiH.1 phiH.2 1509.18429228 -0.02568549 1.03036944 -0.73752250 Threshold: -Variable: Z(t) = + (1) X(t)+ (0)X(t-1)+ (1)X(t-0) -Value: 600 1426 (fixed) Proportion of points in low regime: 42.86% Middle regime: 16.96% High regime: 40.18% Residuals: Min 1Q Median 3Q Max -2536.340 -282.233 -43.187 209.251 2998.392 Fit: residuals variance = 605247, AIC = 1542, MAPE = 59.07% Coefficient(s): Estimate Std. Error t value Pr(>|t|) const.L 24.572906 332.282801 0.0740 0.941194 trend.L 0.225852 3.886426 0.0581 0.953772 phiL.1 2.165512 0.836039 2.5902 0.010995 * phiL.2 -0.362398 0.183649 -1.9733 0.051165 . const.M -66.876690 866.759950 -0.0772 0.938650 trend.M 2.245397 6.360819 0.3530 0.724814 phiM.1 2.191313 0.765415 2.8629 0.005095 ** phiM.2 -0.496035 0.164531 -3.0149 0.003244 ** const.H 1509.184292 367.490470 4.1067 8.110e-05 *** trend.H -0.025685 3.605047 -0.0071 0.994329 phiH.1 1.030369 0.108365 9.5083 9.981e-16 *** phiH.2 -0.737523 0.089643 -8.2274 6.550e-13 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Threshold Variable: Z(t) = + (1) X(t) + (0) X(t-1) Value: 600 1426 (fixed) $set7 Non linear autoregressive model SETAR model ( 3 regimes) Coefficients: Low regime: phiL.1 phiL.2 2.2609406 -0.3622496 Mid regime: phiM.1 phiM.2 2.2743042 -0.4979271 High regime: phiH.1 phiH.2 1.3766159 -0.6640694 Threshold: -Variable: Z(t) = + (1) X(t)+ (0)X(t-1)+ (1)X(t-0) -Value: 600 1426 (fixed) Proportion of points in low regime: 42.86% Middle regime: 16.96% High regime: 40.18% Residuals: Min 1Q Median 3Q Max -2799.5014 -217.0299 -6.1145 318.8831 3240.8095 Fit: residuals variance = 745038, AIC = 1553, MAPE = 59.38% Coefficient(s): Estimate Std. Error t value Pr(>|t|) phiL.1 2.260941 0.544890 4.1493 6.670e-05 *** phiL.2 -0.362250 0.196838 -1.8403 0.06846 . phiM.1 2.274304 0.278901 8.1545 6.854e-13 *** phiM.2 -0.497927 0.159452 -3.1227 0.00230 ** phiH.1 1.376616 0.087726 15.6922 < 2.2e-16 *** phiH.2 -0.664069 0.095180 -6.9770 2.535e-10 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Threshold Variable: Z(t) = + (1) X(t) + (0) X(t-1) Value: 600 1426 (fixed) > sapply(set_all, function(x) tail(coef(x),4)) set0 set1 set2 set3 set4 const.H 1465.133661 1292.8489487 1.030366 1465.133661 1.0524925 phiH.1 1.044104 0.9830795 -0.737548 1.044104 -0.4482403 phiH.2 -0.741993 -0.6362265 600.000000 -0.741993 1388.0000000 th 1388.000000 1426.0000000 1426.000000 1388.000000 3465.0000000 set5 set6 set7 const.H 1.2787370 1.0303694 1.3766159 phiH.1 -0.6923989 -0.7375225 -0.6640694 phiH.2 600.0000000 600.0000000 600.0000000 th 1426.0000000 1426.0000000 1426.0000000 > sapply(set_all, function(x) tail(coef(x, hyperCo=FALSE),4)) set0 set1 set2 set3 set4 phiL.2 -0.4206083 -0.4291513 -0.5028274 -0.4206083 -0.9420740 const.H 1465.1336611 1292.8489487 1507.7972420 1465.1336611 -60.0542362 phiH.1 1.0441036 0.9830795 1.0303664 1.0441036 1.0524925 phiH.2 -0.7419930 -0.6362265 -0.7375480 -0.7419930 -0.4482403 set5 set6 set7 phiL.2 -0.5015428 1509.18429228 2.2743042 const.H 7.8172099 -0.02568549 -0.4979271 phiH.1 1.2787370 1.03036944 1.3766159 phiH.2 -0.6923989 -0.73752250 -0.6640694 > lapply(set_all, function(x) round(vcov(x),9)) $set0 [,1] [,2] [,3] [,4] [,5] [1,] 29032.028788 -36.304591675 -4.257061288 0.000000 0.000000000 [2,] -36.304592 0.095025394 -0.009820489 0.000000 0.000000000 [3,] -4.257061 -0.009820489 0.012258470 0.000000 0.000000000 [4,] 0.000000 0.000000000 0.000000000 85801.127467 -19.348901868 [5,] 0.000000 0.000000000 0.000000000 -19.348902 0.010436985 [6,] 0.000000 0.000000000 0.000000000 -4.768146 -0.004807271 [7,] 0.000000 0.000000000 0.000000000 0.000000 0.000000000 [,6] [,7] [1,] 0.000000000 0 [2,] 0.000000000 0 [3,] 0.000000000 0 [4,] -4.768146333 0 [5,] -0.004807271 0 [6,] 0.007361045 0 [7,] 0.000000000 0 $set1 [,1] [,2] [,3] [,4] [,5] [1,] 28405.440896 2.31326756 -43.14709898 0.000000 0.000000000 [2,] 2.313268 0.03221329 -0.05697597 0.000000 0.000000000 [3,] -43.147099 -0.05697597 0.18437058 0.000000 0.000000000 [4,] 0.000000 0.00000000 0.00000000 102930.310673 -6.813328445 [5,] 0.000000 0.00000000 0.00000000 -6.813328 0.006573628 [6,] 0.000000 0.00000000 0.00000000 -22.029723 -0.003573714 [7,] 0.000000 0.00000000 0.00000000 0.000000 0.000000000 [,6] [,7] [1,] 0.000000000 0 [2,] 0.000000000 0 [3,] 0.000000000 0 [4,] -22.029722638 0 [5,] -0.003573714 0 [6,] 0.010176910 0 [7,] 0.000000000 0 $set2 [,1] [,2] [,3] [,4] [,5] [1,] 57711.704668 -155.69385750 1.46486069 0.00000 0.00000000 [2,] -155.693857 0.66842888 -0.06690375 0.00000 0.00000000 [3,] 1.464861 -0.06690375 0.03245268 0.00000 0.00000000 [4,] 0.000000 0.00000000 0.00000000 612932.47807 -551.62594830 [5,] 0.000000 0.00000000 0.00000000 -551.62595 0.56152924 [6,] 0.000000 0.00000000 0.00000000 -53.66462 0.02394941 [7,] 0.000000 0.00000000 0.00000000 0.00000 0.00000000 [8,] 0.000000 0.00000000 0.00000000 0.00000 0.00000000 [9,] 0.000000 0.00000000 0.00000000 0.00000 0.00000000 [10,] 0.000000 0.00000000 0.00000000 0.00000 0.00000000 [11,] 0.000000 0.00000000 0.00000000 0.00000 0.00000000 [,6] [,7] [,8] [,9] [,10] [,11] [1,] 0.00000000 0.000000 0.000000000 0.000000000 0 0 [2,] 0.00000000 0.000000 0.000000000 0.000000000 0 0 [3,] 0.00000000 0.000000 0.000000000 0.000000000 0 0 [4,] -53.66461618 0.000000 0.000000000 0.000000000 0 0 [5,] 0.02394941 0.000000 0.000000000 0.000000000 0 0 [6,] 0.02596978 0.000000 0.000000000 0.000000000 0 0 [7,] 0.00000000 94492.741915 -21.699246932 -4.604858909 0 0 [8,] 0.00000000 -21.699247 0.011421572 -0.005203298 0 0 [9,] 0.00000000 -4.604859 -0.005203298 0.007803559 0 0 [10,] 0.00000000 0.000000 0.000000000 0.000000000 0 0 [11,] 0.00000000 0.000000 0.000000000 0.000000000 0 0 $set3 [,1] [,2] [,3] [,4] [,5] [1,] 29032.028788 -36.304591675 -4.257061288 0.000000 0.000000000 [2,] -36.304592 0.095025394 -0.009820489 0.000000 0.000000000 [3,] -4.257061 -0.009820489 0.012258470 0.000000 0.000000000 [4,] 0.000000 0.000000000 0.000000000 85801.127467 -19.348901868 [5,] 0.000000 0.000000000 0.000000000 -19.348902 0.010436985 [6,] 0.000000 0.000000000 0.000000000 -4.768146 -0.004807271 [7,] 0.000000 0.000000000 0.000000000 0.000000 0.000000000 [,6] [,7] [1,] 0.000000000 0 [2,] 0.000000000 0 [3,] 0.000000000 0 [4,] -4.768146333 0 [5,] -0.004807271 0 [6,] 0.007361045 0 [7,] 0.000000000 0 $set4 [,1] [,2] [,3] [,4] [,5] [1,] 23916.212199 -29.907256039 -3.506912377 0.00000 0.000000000 [2,] -29.907256 0.078280698 -0.008089993 0.00000 0.000000000 [3,] -3.506912 -0.008089993 0.010098370 0.00000 0.000000000 [4,] 0.000000 0.000000000 0.000000000 273114.76248 -99.629027229 [5,] 0.000000 0.000000000 0.000000000 -99.62903 0.047937435 [6,] 0.000000 0.000000000 0.000000000 -10.45755 -0.006540008 [7,] 0.000000 0.000000000 0.000000000 0.00000 0.000000000 [8,] 0.000000 0.000000000 0.000000000 0.00000 0.000000000 [9,] 0.000000 0.000000000 0.000000000 0.00000 0.000000000 [10,] 0.000000 0.000000000 0.000000000 0.00000 0.000000000 [11,] 0.000000 0.000000000 0.000000000 0.00000 0.000000000 [,6] [,7] [,8] [,9] [,10] [,11] [1,] 0.000000000 0.00000 0.000000e+00 0.000000000 0 0 [2,] 0.000000000 0.00000 0.000000e+00 0.000000000 0 0 [3,] 0.000000000 0.00000 0.000000e+00 0.000000000 0 0 [4,] -10.457545456 0.00000 0.000000e+00 0.000000000 0 0 [5,] -0.006540008 0.00000 0.000000e+00 0.000000000 0 0 [6,] 0.012463130 0.00000 0.000000e+00 0.000000000 0 0 [7,] 0.000000000 690794.63768 -1.109302e+02 -32.890905145 0 0 [8,] 0.000000000 -110.93021 2.688865e-02 -0.003982121 0 0 [9,] 0.000000000 -32.89091 -3.982121e-03 0.012973302 0 0 [10,] 0.000000000 0.00000 0.000000e+00 0.000000000 0 0 [11,] 0.000000000 0.00000 0.000000e+00 0.000000000 0 0 $set5 [,1] [,2] [,3] [,4] [,5] [,6] [1,] 9.18968217 -1.18553064 -0.03140999 0.0000000 0.0000000 0.00000000 [2,] -1.18553064 0.44207818 -0.06922364 0.0000000 0.0000000 0.00000000 [3,] -0.03140999 -0.06922364 0.03783888 0.0000000 0.0000000 0.00000000 [4,] 0.00000000 0.00000000 0.00000000 38.4231149 -2.4500713 -0.06782330 [5,] 0.00000000 0.00000000 0.00000000 -2.4500713 0.2319805 -0.02401570 [6,] 0.00000000 0.00000000 0.00000000 -0.0678233 -0.0240157 0.02487936 [7,] 0.00000000 0.00000000 0.00000000 0.0000000 0.0000000 0.00000000 [8,] 0.00000000 0.00000000 0.00000000 0.0000000 0.0000000 0.00000000 [9,] 0.00000000 0.00000000 0.00000000 0.0000000 0.0000000 0.00000000 [10,] 0.00000000 0.00000000 0.00000000 0.0000000 0.0000000 0.00000000 [11,] 0.00000000 0.00000000 0.00000000 0.0000000 0.0000000 0.00000000 [,7] [,8] [,9] [,10] [,11] [1,] 0.00000000 0.000000000 0.000000000 0 0 [2,] 0.00000000 0.000000000 0.000000000 0 0 [3,] 0.00000000 0.000000000 0.000000000 0 0 [4,] 0.00000000 0.000000000 0.000000000 0 0 [5,] 0.00000000 0.000000000 0.000000000 0 0 [6,] 0.00000000 0.000000000 0.000000000 0 0 [7,] 10.58473037 -0.132530884 -0.038359029 0 0 [8,] -0.13253088 0.009153886 -0.006807203 0 0 [9,] -0.03835903 -0.006807203 0.008961118 0 0 [10,] 0.00000000 0.000000000 0.000000000 0 0 [11,] 0.00000000 0.000000000 0.000000000 0 0 $set6 [,1] [,2] [,3] [,4] [,5] [1,] 110411.860171 -878.34281592 -184.55551028 5.80465920 0.00000 [2,] -878.342816 15.10430440 0.42102480 -0.07392038 0.00000 [3,] -184.555510 0.42102480 0.69896173 -0.07084565 0.00000 [4,] 5.804659 -0.07392038 -0.07084565 0.03372706 0.00000 [5,] 0.000000 0.00000000 0.00000000 0.00000000 751272.81108 [6,] 0.000000 0.00000000 0.00000000 0.00000000 -2213.56445 [7,] 0.000000 0.00000000 0.00000000 0.00000000 -534.98008 [8,] 0.000000 0.00000000 0.00000000 0.00000000 -61.86953 [9,] 0.000000 0.00000000 0.00000000 0.00000000 0.00000 [10,] 0.000000 0.00000000 0.00000000 0.00000000 0.00000 [11,] 0.000000 0.00000000 0.00000000 0.00000000 0.00000 [12,] 0.000000 0.00000000 0.00000000 0.00000000 0.00000 [13,] 0.000000 0.00000000 0.00000000 0.00000000 0.00000 [14,] 0.000000 0.00000000 0.00000000 0.00000000 0.00000 [,6] [,7] [,8] [,9] [,10] [1,] 0.0000000 0.00000000 0.00000000 0.000000 0.000000e+00 [2,] 0.0000000 0.00000000 0.00000000 0.000000 0.000000e+00 [3,] 0.0000000 0.00000000 0.00000000 0.000000 0.000000e+00 [4,] 0.0000000 0.00000000 0.00000000 0.000000 0.000000e+00 [5,] -2213.5644547 -534.98007873 -61.86953331 0.000000 0.000000e+00 [6,] 40.4600133 -0.58779526 0.12238739 0.000000 0.000000e+00 [7,] -0.5877953 0.58585947 0.02284488 0.000000 0.000000e+00 [8,] 0.1223874 0.02284488 0.02707029 0.000000 0.000000e+00 [9,] 0.0000000 0.00000000 0.00000000 135049.245368 -7.018209e+02 [10,] 0.0000000 0.00000000 0.00000000 -701.820920 1.299637e+01 [11,] 0.0000000 0.00000000 0.00000000 -22.225149 -1.561187e-03 [12,] 0.0000000 0.00000000 0.00000000 -4.037878 -1.289736e-02 [13,] 0.0000000 0.00000000 0.00000000 0.000000 0.000000e+00 [14,] 0.0000000 0.00000000 0.00000000 0.000000 0.000000e+00 [,11] [,12] [,13] [,14] [1,] 0.000000000 0.000000000 0 0 [2,] 0.000000000 0.000000000 0 0 [3,] 0.000000000 0.000000000 0 0 [4,] 0.000000000 0.000000000 0 0 [5,] 0.000000000 0.000000000 0 0 [6,] 0.000000000 0.000000000 0 0 [7,] 0.000000000 0.000000000 0 0 [8,] 0.000000000 0.000000000 0 0 [9,] -22.225148985 -4.037878214 0 0 [10,] -0.001561187 -0.012897364 0 0 [11,] 0.011742947 -0.005348071 0 0 [12,] -0.005348071 0.008035803 0 0 [13,] 0.000000000 0.000000000 0 0 [14,] 0.000000000 0.000000000 0 0 $set7 [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.29690548 -0.07524455 0.00000000 0.00000000 0.000000000 0.000000000 [2,] -0.07524455 0.03874532 0.00000000 0.00000000 0.000000000 0.000000000 [3,] 0.00000000 0.00000000 0.07778567 -0.02910196 0.000000000 0.000000000 [4,] 0.00000000 0.00000000 -0.02910196 0.02542489 0.000000000 0.000000000 [5,] 0.00000000 0.00000000 0.00000000 0.00000000 0.007695840 -0.007483299 [6,] 0.00000000 0.00000000 0.00000000 0.00000000 -0.007483299 0.009059143 [7,] 0.00000000 0.00000000 0.00000000 0.00000000 0.000000000 0.000000000 [8,] 0.00000000 0.00000000 0.00000000 0.00000000 0.000000000 0.000000000 [,7] [,8] [1,] 0 0 [2,] 0 0 [3,] 0 0 [4,] 0 0 [5,] 0 0 [6,] 0 0 [7,] 0 0 [8,] 0 0 > > lapply(set_all, function(x) window(residuals(x), end=c(1830))) $set0 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA -48.33439 -356.39864 -308.62192 462.08943 [7] 611.88976 2469.78961 194.30710 353.21775 $set1 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA -110.69430 -209.84115 66.03686 602.54884 [7] 800.31772 2583.40948 313.80691 199.00119 $set2 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA -48.62731 -313.87318 -223.86083 435.81669 [7] 601.42256 2468.54660 215.82403 352.13702 $set3 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA -48.33439 -356.39864 -308.62192 462.08943 [7] 611.88976 2469.78961 194.30710 353.21775 $set4 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA -48.33439 -356.39864 -308.62192 822.48884 [7] -30.13067 3133.34951 515.77911 91.10833 $set5 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA -25.90285 -303.95097 -104.89652 1506.67363 [7] 1302.88544 2826.47540 15.48885 299.64132 $set6 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA -37.44301 -304.51927 -83.31229 434.50563 [7] 600.11764 2467.22964 214.49832 350.78870 $set7 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA -43.31677 -335.36810 -214.63159 1368.89606 [7] 1024.06904 2408.99270 -622.76342 -290.68403 > lapply(set_all, function(x) window(fitted(x), end=c(1830))) $set0 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 633.3344 1227.3986 1783.6219 2358.9106 3316.1102 [8] 3473.2104 4755.6929 2223.7823 $set1 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 695.6943 1080.8412 1408.9631 2218.4512 3127.6823 [8] 3359.5905 4636.1931 2377.9988 $set2 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 633.6273 1184.8732 1698.8608 2385.1833 3326.5774 [8] 3474.4534 4734.1760 2224.8630 $set3 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 633.3344 1227.3986 1783.6219 2358.9106 3316.1102 [8] 3473.2104 4755.6929 2223.7823 $set4 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 633.3344 1227.3986 1783.6219 1998.5112 3958.1307 [8] 2809.6505 4434.2209 2485.8917 $set5 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 610.9028 1174.9510 1579.8965 1314.3264 2625.1146 [8] 3116.5246 4934.5111 2277.3587 $set6 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 622.443 1175.519 1558.312 2386.494 3327.882 3475.770 [9] 4735.502 2226.211 $set7 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 628.3168 1206.3681 1689.6316 1452.1039 2903.9310 [8] 3534.0073 5572.7634 2867.6840 > > lapply(set_all, function(x) window(regime(x), end=c(1830))) $set0 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 1 1 1 2 2 2 2 2 $set1 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 1 1 1 1 2 2 2 2 $set2 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 1 1 2 3 3 3 3 3 $set3 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 1 1 1 2 2 2 2 2 $set4 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 1 1 1 2 2 3 3 3 $set5 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 1 1 2 3 3 3 3 3 $set6 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 1 1 2 3 3 3 3 3 $set7 Time Series: Start = 1821 End = 1830 Frequency = 1 [1] NA NA 1 1 2 3 3 3 3 3 > lapply(set_all, function(x) window(regime(x, initVal=FALSE), end=c(1830))) $set0 Time Series: Start = 1823 End = 1830 Frequency = 1 [1] 1 1 1 2 2 2 2 2 $set1 Time Series: Start = 1823 End = 1830 Frequency = 1 [1] 1 1 1 1 2 2 2 2 $set2 Time Series: Start = 1823 End = 1830 Frequency = 1 [1] 1 1 2 3 3 3 3 3 $set3 Time Series: Start = 1823 End = 1830 Frequency = 1 [1] 1 1 1 2 2 2 2 2 $set4 Time Series: Start = 1823 End = 1830 Frequency = 1 [1] 1 1 1 2 2 3 3 3 $set5 Time Series: Start = 1823 End = 1830 Frequency = 1 [1] 1 1 2 3 3 3 3 3 $set6 Time Series: Start = 1823 End = 1830 Frequency = 1 [1] 1 1 2 3 3 3 3 3 $set7 Time Series: Start = 1823 End = 1830 Frequency = 1 [1] 1 1 2 3 3 3 3 3 > sapply(set_all, function(x) head(regime(x, time=FALSE),10)) set0 set1 set2 set3 set4 set5 set6 set7 [1,] NA NA NA NA NA NA NA NA [2,] NA NA NA NA NA NA NA NA [3,] 1 1 1 1 1 1 1 1 [4,] 1 1 1 1 1 1 1 1 [5,] 1 1 2 1 1 2 2 2 [6,] 2 1 3 2 2 3 3 3 [7,] 2 2 3 2 2 3 3 3 [8,] 2 2 3 2 3 3 3 3 [9,] 2 2 3 2 3 3 3 3 [10,] 2 2 3 2 3 3 3 3 > sapply(set_all, function(x) head(regime(x, initVal=FALSE, time=FALSE),10)) set0 set1 set2 set3 set4 set5 set6 set7 [1,] 1 1 1 1 1 1 1 1 [2,] 1 1 1 1 1 1 1 1 [3,] 1 1 2 1 1 2 2 2 [4,] 2 1 3 2 2 3 3 3 [5,] 2 2 3 2 2 3 3 3 [6,] 2 2 3 2 3 3 3 3 [7,] 2 2 3 2 3 3 3 3 [8,] 2 2 3 2 3 3 3 3 [9,] 2 2 3 2 2 3 3 3 [10,] 1 2 1 1 1 1 1 1 > > sapply(set_all, function(x) head(x$model,2)) $set0 yy const.L phiL.1 phiL.2 const.H phiH.1 phiH.2 1 585 1 321 269 0 0 0 2 871 1 585 321 0 0 0 $set1 yy const.L phiL.1 phiL.2 const.H phiH.1 phiH.2 1 585 1 321 269 0 0 0 2 871 1 585 321 0 0 0 $set2 yy const.L phiL.1 phiL.2 const.M phiM.1 phiM.2 const.H phiH.1 phiH.2 1 585 1 321 269 0 0 0 0 0 0 2 871 1 585 321 0 0 0 0 0 0 $set3 yy const.L phiL.1 phiL.2 const.H phiH.1 phiH.2 1 585 1 321 269 0 0 0 2 871 1 585 321 0 0 0 $set4 yy const.L phiL.1 phiL.2 const.M phiM.1 phiM.2 const.H phiH.1 phiH.2 1 585 1 321 269 0 0 0 0 0 0 2 871 1 585 321 0 0 0 0 0 0 $set5 yy trend.L phiL.1 phiL.2 trend.M phiM.1 phiM.2 trend.H phiH.1 phiH.2 1 585 1 321 269 0 0 0 0 0 0 2 871 2 585 321 0 0 0 0 0 0 $set6 yy const.L trend.L phiL.1 phiL.2 const.M trend.M phiM.1 phiM.2 const.H 1 585 1 1 321 269 0 0 0 0 0 2 871 1 2 585 321 0 0 0 0 0 trend.H phiH.1 phiH.2 1 0 0 0 2 0 0 0 $set7 yy phiL.1 phiL.2 phiM.1 phiM.2 phiH.1 phiH.2 1 585 321 269 0 0 0 0 2 871 585 321 0 0 0 0 > > lapply(set_all[c(1,2,4)], toLatex) $set0 \[ X_{t-1} = \left\{\begin{array}{lr} - 2.444 + 2.333 X_{t-0}- 0.421 X_{t-1}& Z_t \leq + 1388\\ + 1465.134 + 1.044 X_{t-0}- 0.742 X_{t-1}& Z_t > + 1388\\ \end{array}\right. \] \[ Z_t = X_{t-0} \] $set1 \[ X_{t-1} = \left\{\begin{array}{lr} + 315.698 + 1.543 X_{t-0}- 0.429 X_{t-1}& Z_t \leq + 1426\\ + 1292.849 + 0.983 X_{t-0}- 0.636 X_{t-1}& Z_t > + 1426\\ \end{array}\right. \] \[ Z_t = X_{t-1} \] $set3 \[ X_{t-1} = \left\{\begin{array}{lr} - 2.444 + 2.333 X_{t-0}- 0.421 X_{t-1}& Z_t \leq + 1388\\ + 1465.134 + 1.044 X_{t-0}- 0.742 X_{t-1}& Z_t > + 1388\\ \end{array}\right. \] \[ Z_t = X_{t-0} \] > > ## coefs > sapply(set_all, function(x) tail(coef(x, regime = "L"),4)) $set0 const.L phiL.1 phiL.2 -2.4440208 2.3330905 -0.4206083 $set1 const.L phiL.1 phiL.2 315.6983921 1.5434194 -0.4291513 $set2 const.L phiL.1 phiL.2 37.7065835 2.1592162 -0.3612925 $set3 const.L phiL.1 phiL.2 -2.4440208 2.3330905 -0.4206083 $set4 const.L phiL.1 phiL.2 -2.4440208 2.3330905 -0.4206083 $set5 trend.L phiL.1 phiL.2 0.4213327 2.2065858 -0.3636897 $set6 const.L trend.L phiL.1 phiL.2 24.5729061 0.2258515 2.1655117 -0.3623979 $set7 phiL.1 phiL.2 2.2609406 -0.3622496 > sapply(set_all, function(x) tail(coef(x, regime = "H"),4)) $set0 const.H phiH.1 phiH.2 1465.133661 1.044104 -0.741993 $set1 const.H phiH.1 phiH.2 1292.8489487 0.9830795 -0.6362265 $set2 const.H phiH.1 phiH.2 1507.797242 1.030366 -0.737548 $set3 const.H phiH.1 phiH.2 1465.133661 1.044104 -0.741993 $set4 const.H phiH.1 phiH.2 -60.0542362 1.0524925 -0.4482403 $set5 trend.H phiH.1 phiH.2 7.8172099 1.2787370 -0.6923989 $set6 const.H trend.H phiH.1 phiH.2 1509.18429228 -0.02568549 1.03036944 -0.73752250 $set7 phiH.1 phiH.2 1.3766159 -0.6640694 > > > ###SelectSetar > selectSETAR(lynx, m=2, d=1, trace=TRUE, include = "const", common="none", model="TAR", nthresh=1,trim=0.15,criterion = "SSR", plot=FALSE,max.iter=2) Using maximum autoregressive order for low regime: mL = 2 Using maximum autoregressive order for high regime: mH = 2 Searching on 74 possible threshold values within regimes with sufficient ( 15% ) number of observations Searching on 74 combinations of thresholds (74) and thDelay (1) Results of the grid search for 1 threshold Conditional on m= 2 thDelay th SSR 1 0 1388 67408458 2 0 1307 67518614 3 0 871 67933996 4 0 1000 67974066 5 0 1033 68019679 6 0 1132 68097638 7 0 1292 68230420 8 0 808 68250304 9 0 957 68288887 10 0 784 68734556 > selectSETAR(lynx, m=2, d=1, trace=TRUE, include = "const", common="none", model="TAR", nthresh=2,trim=0.15,criterion = "SSR", plot=FALSE,max.iter=3) Using maximum autoregressive order for low regime: mL = 2 Using maximum autoregressive order for high regime: mH = 2 Using maximum autoregressive order for middle regime: mM = 2 Searching on 74 possible threshold values within regimes with sufficient ( 15% ) number of observations Searching on 74 combinations of thresholds (74) and thDelay (1) Result of the one threshold search: -Thresh: 1388 -Delay: 0 - SSR 67408458 Second best: 2536 (conditionnal on th= 1388 and Delay= 0 ) SSR/AIC: 60665581 Second best: 1132 (conditionnal on th= 2536 and Delay= 0 ) SSR/AIC: 59831519 Second best: 2536 (conditionnal on th= 1132 and Delay= 0 ) SSR/AIC: 59831519 Results of the grid search for 1 threshold Conditional on m= 2 thDelay th SSR 1 0 1388 67408458 2 0 1307 67518614 3 0 871 67933996 4 0 1000 67974066 5 0 1033 68019679 Results of the grid search for 2 thresholds Conditional on thDelay = 0 and m = 2 th1 th2 SSR 1 1132 2536 59831519 Overall best results: thDelay th1 th2 SSR 0 1132 2536 59831519 With lags: -ML: 1 2 -MM: 1 2 -MH: 1 2 > selectSETAR(lynx, m=2, d=1, th=MakeThSpec(ngrid="Half"), common="none",trim=0.15,criterion = "SSR", plot=FALSE) Using maximum autoregressive order for low regime: mL = 2 Using maximum autoregressive order for high regime: mH = 2 Searching on 39 possible threshold values within regimes with sufficient ( 15% ) number of observations Searching on 39 combinations of thresholds (39) and thDelay (1) Results of the grid search for 1 threshold Conditional on m= 2 thDelay th SSR 1 0 1388 67408458 2 0 1033 68019679 3 0 1292 68230420 4 0 808 68250304 5 0 957 68288887 6 0 758 68951854 7 0 731 69101879 8 0 1475 69183671 9 0 684 70591358 10 0 662 70956483 > selectSETAR(lynx, m=2, d=1, th=MakeThSpec(ngrid=20), common="none",trim=0.15,criterion = "SSR", plot=FALSE) Using maximum autoregressive order for low regime: mL = 2 Using maximum autoregressive order for high regime: mH = 2 Searching on 20 possible threshold values within regimes with sufficient ( 15% ) number of observations Searching on 20 combinations of thresholds (20) and thDelay (1) Results of the grid search for 1 threshold Conditional on m= 2 thDelay th SSR 1 0 1388 67408458 2 0 1033 68019679 3 0 808 68250304 4 0 731 69101879 5 0 662 70956483 6 0 1590 71081206 7 0 1676 71697226 8 0 546 72271914 9 0 473 73478005 10 0 399 73865774 > selectSETAR(lynx, m=2, d=1, th=MakeThSpec(exact=1388), common="none",trim=0.15,criterion = "SSR", plot=FALSE) Using maximum autoregressive order for low regime: mL = 2 Using maximum autoregressive order for high regime: mH = 2 Searching on 1 combinations of thresholds (1) and thDelay (1) Results of the grid search for 1 threshold Conditional on m= 2 thDelay th SSR 1 0 1388 67408458 > selectSETAR(lynx, m=2, d=1, th=MakeThSpec(around=1388, ngrid=20), common="none",criterion = "SSR", plot=FALSE) Using maximum autoregressive order for low regime: mL = 2 Using maximum autoregressive order for high regime: mH = 2 Searching within 20 values around 1388 Searching on 21 combinations of thresholds (21) and thDelay (1) Results of the grid search for 1 threshold Conditional on m= 2 thDelay th SSR 1 0 1388 67408458 2 0 1307 67518614 3 0 871 67933996 4 0 1000 67974066 5 0 1033 68019679 6 0 1132 68097638 7 0 1292 68230420 8 0 808 68250304 9 0 957 68288887 10 0 784 68734556 > selectSETAR(lynx, m=2, d=1, th=MakeThSpec(int=c(957, 1824)), common="none",criterion = "SSR", plot=FALSE) Using maximum autoregressive order for low regime: mL = 2 Using maximum autoregressive order for high regime: mH = 2 Searching within 15 values between 957 and 1824 Searching on 15 combinations of thresholds (15) and thDelay (1) Results of the grid search for 1 threshold Conditional on m= 2 thDelay th SSR 1 0 1388 67408458 2 0 1307 67518614 3 0 1000 67974066 4 0 1033 68019679 5 0 1132 68097638 6 0 1292 68230420 7 0 957 68288887 8 0 1475 69183671 9 0 1426 69383627 10 0 1590 71081206 > selectSETAR(lynx, m=2, d=1, th=MakeThSpec(int=c(957, 1824), ngrid=5), common="none",criterion = "SSR", plot=FALSE) Using maximum autoregressive order for low regime: mL = 2 Using maximum autoregressive order for high regime: mH = 2 Searching within 5 values between 957 and 1824 Searching on 5 combinations of thresholds (5) and thDelay (1) Results of the grid search for 1 threshold Conditional on m= 2 thDelay th SSR 1 0 1132 68097638 2 0 957 68288887 3 0 1426 69383627 4 0 1594 71748426 5 0 1824 73711421 > selectSETAR(lynx, m=2, d=1, th=MakeThSpec(int=c(957, 1824), ngrid="Half"), common="none",criterion = "SSR", plot=FALSE) Using maximum autoregressive order for low regime: mL = 2 Using maximum autoregressive order for high regime: mH = 2 Searching within 7.5 values between 957 and 1824 Searching on 8 combinations of thresholds (8) and thDelay (1) Results of the grid search for 1 threshold Conditional on m= 2 thDelay th SSR 1 0 1388 67408458 2 0 1033 68019679 3 0 1292 68230420 4 0 957 68288887 5 0 1475 69183671 6 0 1590 71081206 7 0 1623 71572143 8 0 1824 73711421 > > > ## setar.sim > set_all_boot <- lapply(set_all, function(x) setar.boot(x, boot.scheme = "check", returnStarting = TRUE, round_digits=10)) > set_all_boot_check <- sapply(set_all_boot, function(x) all.equal(x, as.numeric(lynx))) > > vals_df <- bind_cols(set_all_boot) %>% + mutate(n_row = 1:n()) %>% + gather(model, value, -n_row) %>% + mutate(original = rep(as.numeric(lynx), length(set_all_boot)), + diff = abs(value-original) < 1e-010) > > if(FALSE) { + vals_df %>% + filter(value!=original) %>% + group_by(model) %>% + mutate(rmse = sqrt(mean(value-original)^2)) %>% + filter(n_row ==min(n_row)) %>% + select(-rmse) + } > > proc.time() user system elapsed 2.39 0.43 2.81