ModelBuildMultivariateVAR.Rmdlibrary(tswgewrapped)
file = system.file("extdata", "USeconomic.csv", package = "tswgewrapped", mustWork = TRUE)
data = read.csv(file, header = TRUE, stringsAsFactors = FALSE, check.names = FALSE)
names(data) = gsub("[(|)]", "", colnames(data))lag.max = 10
models = list("AIC None" = list(select = "aic", trend_type = "none", lag.max = lag.max),
"AIC Trend" = list(select = "aic", trend_type = "trend", lag.max = lag.max),
"AIC Both" = list(select = "aic", trend_type = "both", lag.max = lag.max),
"BIC None" = list(select = "bic", trend_type = "none", lag.max = lag.max),
"BIC Trend" = list(select = "bic", trend_type = "trend", lag.max = lag.max),
"BIC Both" = list(select = "bic", trend_type = "both", lag.max = lag.max))
var_interest = 'logGNP'mdl_build = ModelBuildMultivariateVAR$new(data = data, var_interest = var_interest,
mdl_list = models, verbose = 1)
#>
#> Model: AIC None
#> Trend type: none
#> Seasonality:
#> VARselect Object:
#> $selection
#> AIC(n) HQ(n) SC(n) FPE(n)
#> 3 2 2 3
#>
#> $criteria
#> 1 2 3 4
#> AIC(n) -3.979949e+01 -4.043610e+01 -4.049031e+01 -4.041841e+01
#> HQ(n) -3.965317e+01 -4.014345e+01 -4.005134e+01 -3.983312e+01
#> SC(n) -3.943933e+01 -3.971577e+01 -3.940982e+01 -3.897776e+01
#> FPE(n) 5.192033e-18 2.748673e-18 2.607880e-18 2.811272e-18
#> 5 6 7 8
#> AIC(n) -4.037845e+01 -4.041713e+01 -4.032619e+01 -4.034898e+01
#> HQ(n) -3.964684e+01 -3.953920e+01 -3.930193e+01 -3.917840e+01
#> SC(n) -3.857764e+01 -3.825616e+01 -3.780505e+01 -3.746768e+01
#> FPE(n) 2.941472e-18 2.852623e-18 3.159796e-18 3.136181e-18
#> 9 10
#> AIC(n) -4.033399e+01 -4.037318e+01
#> HQ(n) -3.901708e+01 -3.890995e+01
#> SC(n) -3.709252e+01 -3.677155e+01
#> FPE(n) 3.247802e-18 3.203453e-18
#>
#>
#> Lag K to use for the VAR Model: 3
#> Printing summary of the VAR fit for the variable of interest: logGNP
#>
#> Call:
#> lm(formula = y ~ -1 + ., data = datamat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.0227911 -0.0059353 0.0008095 0.0056885 0.0214455
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> logM1.l1 0.026269 0.114301 0.230 0.8186
#> logGNP.l1 1.057405 0.098851 10.697 <2e-16 ***
#> rs.l1 -0.224098 0.134021 -1.672 0.0971 .
#> rl.l1 0.491966 0.274355 1.793 0.0754 .
#> logM1.l2 0.070436 0.175106 0.402 0.6882
#> logGNP.l2 0.009095 0.133053 0.068 0.9456
#> rs.l2 -0.149187 0.186274 -0.801 0.4248
#> rl.l2 -0.434827 0.385994 -1.127 0.2622
#> logM1.l3 -0.117121 0.095870 -1.222 0.2242
#> logGNP.l3 -0.047856 0.092147 -0.519 0.6045
#> rs.l3 0.081275 0.148235 0.548 0.5845
#> rl.l3 0.014681 0.296199 0.050 0.9606
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.008783 on 121 degrees of freedom
#> Multiple R-squared: 1, Adjusted R-squared: 1
#> F-statistic: 8.734e+06 on 12 and 121 DF, p-value: < 2.2e-16
#>
#>
#> Model: AIC Trend
#> Trend type: trend
#> Seasonality:
#> VARselect Object:
#> $selection
#> AIC(n) HQ(n) SC(n) FPE(n)
#> 3 2 2 3
#>
#> $criteria
#> 1 2 3 4
#> AIC(n) -3.976174e+01 -4.044348e+01 -4.049058e+01 -4.041902e+01
#> HQ(n) -3.957884e+01 -4.011426e+01 -4.001503e+01 -3.979715e+01
#> SC(n) -3.931154e+01 -3.963312e+01 -3.932005e+01 -3.888833e+01
#> FPE(n) 5.392212e-18 2.729242e-18 2.608833e-18 2.812659e-18
#> 5 6 7 8
#> AIC(n) -4.038139e+01 -4.045602e+01 -4.037132e+01 -4.040225e+01
#> HQ(n) -3.961320e+01 -3.954150e+01 -3.931048e+01 -3.919508e+01
#> SC(n) -3.849054e+01 -3.820500e+01 -3.776014e+01 -3.743090e+01
#> FPE(n) 2.937907e-18 2.750681e-18 3.030708e-18 2.986986e-18
#> 9 10
#> AIC(n) -4.040127e+01 -4.045800e+01
#> HQ(n) -3.904778e+01 -3.895819e+01
#> SC(n) -3.706976e+01 -3.676633e+01
#> FPE(n) 3.054176e-18 2.964556e-18
#>
#>
#> Lag K to use for the VAR Model: 3
#> Printing summary of the VAR fit for the variable of interest: logGNP
#>
#> Call:
#> lm(formula = y ~ -1 + ., data = datamat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.0234304 -0.0053917 0.0003362 0.0059404 0.0227138
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> logM1.l1 0.0155410 0.1127942 0.138 0.8906
#> logGNP.l1 1.0329596 0.0981286 10.527 <2e-16 ***
#> rs.l1 -0.2865593 0.1353594 -2.117 0.0363 *
#> rl.l1 0.5689100 0.2728852 2.085 0.0392 *
#> logM1.l2 0.0998010 0.1731782 0.576 0.5655
#> logGNP.l2 0.0347234 0.1317220 0.264 0.7925
#> rs.l2 -0.1180638 0.1842183 -0.641 0.5228
#> rl.l2 -0.4921439 0.3814806 -1.290 0.1995
#> logM1.l3 -0.1859015 0.0999133 -1.861 0.0652 .
#> logGNP.l3 -0.0061578 0.0929415 -0.066 0.9473
#> rs.l3 0.0392050 0.1474719 0.266 0.7908
#> rl.l3 -0.0323000 0.2928387 -0.110 0.9124
#> trend -0.0002258 0.0001064 -2.122 0.0359 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.008658 on 120 degrees of freedom
#> Multiple R-squared: 1, Adjusted R-squared: 1
#> F-statistic: 8.296e+06 on 13 and 120 DF, p-value: < 2.2e-16
#> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [4].
#>
#> Model: AIC Both
#> Trend type: both
#> Seasonality:
#> VARselect Object:
#> $selection
#> AIC(n) HQ(n) SC(n) FPE(n)
#> 10 2 2 10
#>
#> $criteria
#> 1 2 3 4
#> AIC(n) -3.982007e+01 -4.053699e+01 -4.057493e+01 -4.052772e+01
#> HQ(n) -3.960059e+01 -4.017118e+01 -4.006280e+01 -3.986926e+01
#> SC(n) -3.927983e+01 -3.963658e+01 -3.931436e+01 -3.890698e+01
#> FPE(n) 5.087308e-18 2.486513e-18 2.399570e-18 2.526125e-18
#> 5 6 7 8
#> AIC(n) -4.047172e+01 -4.050251e+01 -4.042691e+01 -4.048431e+01
#> HQ(n) -3.966695e+01 -3.955141e+01 -3.932949e+01 -3.924056e+01
#> SC(n) -3.849083e+01 -3.816145e+01 -3.772569e+01 -3.742293e+01
#> FPE(n) 2.689264e-18 2.632861e-18 2.877408e-18 2.764981e-18
#> 9 10
#> AIC(n) -4.055474e+01 -4.072060e+01
#> HQ(n) -3.916467e+01 -3.918421e+01
#> SC(n) -3.713319e+01 -3.693889e+01
#> FPE(n) 2.635876e-18 2.297582e-18
#>
#>
#> Lag K to use for the VAR Model: 10
#> Printing summary of the VAR fit for the variable of interest: logGNP
#>
#> Call:
#> lm(formula = y ~ -1 + ., data = datamat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.0215701 -0.0045782 -0.0001255 0.0045321 0.0191265
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> logM1.l1 0.0214370 0.1412448 0.152 0.8797
#> logGNP.l1 0.9230416 0.1180599 7.818 1.4e-11 ***
#> rs.l1 -0.0930221 0.1822500 -0.510 0.6111
#> rl.l1 0.2235868 0.3419400 0.654 0.5150
#> logM1.l2 0.3083456 0.2262523 1.363 0.1766
#> logGNP.l2 -0.0372962 0.1599005 -0.233 0.8161
#> rs.l2 -0.1715295 0.2667294 -0.643 0.5219
#> rl.l2 -0.2970080 0.4138644 -0.718 0.4750
#> logM1.l3 -0.5289338 0.2265162 -2.335 0.0219 *
#> logGNP.l3 -0.1303707 0.1543752 -0.845 0.4008
#> rs.l3 0.0821617 0.2954309 0.278 0.7816
#> rl.l3 0.7072648 0.4357963 1.623 0.1084
#> logM1.l4 0.0766561 0.2389938 0.321 0.7492
#> logGNP.l4 0.0795464 0.1565988 0.508 0.6128
#> rs.l4 -0.3737113 0.2945083 -1.269 0.2080
#> rl.l4 -0.3340966 0.4464294 -0.748 0.4563
#> logM1.l5 0.1482905 0.2442165 0.607 0.5453
#> logGNP.l5 0.0972230 0.1565536 0.621 0.5363
#> rs.l5 -0.1840051 0.2871941 -0.641 0.5235
#> rl.l5 -0.2726578 0.4360683 -0.625 0.5335
#> logM1.l6 -0.0045692 0.2447589 -0.019 0.9852
#> logGNP.l6 0.1750043 0.1564919 1.118 0.2666
#> rs.l6 0.2954565 0.2721934 1.085 0.2808
#> rl.l6 -0.1887511 0.4447874 -0.424 0.6724
#> logM1.l7 -0.1346225 0.2397343 -0.562 0.5759
#> logGNP.l7 -0.0089335 0.1563448 -0.057 0.9546
#> rs.l7 -0.3284367 0.2702839 -1.215 0.2277
#> rl.l7 0.1815723 0.4857967 0.374 0.7095
#> logM1.l8 0.2044394 0.2453420 0.833 0.4070
#> logGNP.l8 -0.1242368 0.1569221 -0.792 0.4308
#> rs.l8 0.4117854 0.2765264 1.489 0.1402
#> rl.l8 -0.5335975 0.4859274 -1.098 0.2753
#> logM1.l9 -0.0729486 0.2365466 -0.308 0.7585
#> logGNP.l9 0.0511809 0.1486065 0.344 0.7314
#> rs.l9 0.1154858 0.2439825 0.473 0.6372
#> rl.l9 -0.1193337 0.4895767 -0.244 0.8080
#> logM1.l10 -0.1535998 0.1459102 -1.053 0.2955
#> logGNP.l10 0.0049021 0.1163395 0.042 0.9665
#> rs.l10 0.2452777 0.2004494 1.224 0.2245
#> rl.l10 -0.2537361 0.4113110 -0.617 0.5390
#> const 0.6423768 0.4222415 1.521 0.1319
#> trend 0.0004486 0.0006232 0.720 0.4736
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.008479 on 84 degrees of freedom
#> Multiple R-squared: 0.9994, Adjusted R-squared: 0.9991
#> F-statistic: 3276 on 41 and 84 DF, p-value: < 2.2e-16
#>
#>
#> Model: BIC None
#> Trend type: none
#> Seasonality:
#> VARselect Object:
#> $selection
#> AIC(n) HQ(n) SC(n) FPE(n)
#> 3 2 2 3
#>
#> $criteria
#> 1 2 3 4
#> AIC(n) -3.979949e+01 -4.043610e+01 -4.049031e+01 -4.041841e+01
#> HQ(n) -3.965317e+01 -4.014345e+01 -4.005134e+01 -3.983312e+01
#> SC(n) -3.943933e+01 -3.971577e+01 -3.940982e+01 -3.897776e+01
#> FPE(n) 5.192033e-18 2.748673e-18 2.607880e-18 2.811272e-18
#> 5 6 7 8
#> AIC(n) -4.037845e+01 -4.041713e+01 -4.032619e+01 -4.034898e+01
#> HQ(n) -3.964684e+01 -3.953920e+01 -3.930193e+01 -3.917840e+01
#> SC(n) -3.857764e+01 -3.825616e+01 -3.780505e+01 -3.746768e+01
#> FPE(n) 2.941472e-18 2.852623e-18 3.159796e-18 3.136181e-18
#> 9 10
#> AIC(n) -4.033399e+01 -4.037318e+01
#> HQ(n) -3.901708e+01 -3.890995e+01
#> SC(n) -3.709252e+01 -3.677155e+01
#> FPE(n) 3.247802e-18 3.203453e-18
#>
#>
#> Lag K to use for the VAR Model: 2
#> Printing summary of the VAR fit for the variable of interest: logGNP
#>
#> Call:
#> lm(formula = y ~ -1 + ., data = datamat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.023513 -0.005879 0.000679 0.005320 0.022638
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> logM1.l1 0.08701 0.08370 1.040 0.3006
#> logGNP.l1 1.04867 0.08880 11.810 <2e-16 ***
#> rs.l1 -0.18282 0.12712 -1.438 0.1528
#> rl.l1 0.49240 0.26683 1.845 0.0673 .
#> logM1.l2 -0.10798 0.08225 -1.313 0.1917
#> logGNP.l2 -0.02954 0.08993 -0.328 0.7431
#> rs.l2 -0.17797 0.13646 -1.304 0.1946
#> rl.l2 -0.35810 0.26028 -1.376 0.1713
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.008673 on 126 degrees of freedom
#> Multiple R-squared: 1, Adjusted R-squared: 1
#> F-statistic: 1.352e+07 on 8 and 126 DF, p-value: < 2.2e-16
#>
#>
#> Model: BIC Trend
#> Trend type: trend
#> Seasonality:
#> VARselect Object:
#> $selection
#> AIC(n) HQ(n) SC(n) FPE(n)
#> 3 2 2 3
#>
#> $criteria
#> 1 2 3 4
#> AIC(n) -3.976174e+01 -4.044348e+01 -4.049058e+01 -4.041902e+01
#> HQ(n) -3.957884e+01 -4.011426e+01 -4.001503e+01 -3.979715e+01
#> SC(n) -3.931154e+01 -3.963312e+01 -3.932005e+01 -3.888833e+01
#> FPE(n) 5.392212e-18 2.729242e-18 2.608833e-18 2.812659e-18
#> 5 6 7 8
#> AIC(n) -4.038139e+01 -4.045602e+01 -4.037132e+01 -4.040225e+01
#> HQ(n) -3.961320e+01 -3.954150e+01 -3.931048e+01 -3.919508e+01
#> SC(n) -3.849054e+01 -3.820500e+01 -3.776014e+01 -3.743090e+01
#> FPE(n) 2.937907e-18 2.750681e-18 3.030708e-18 2.986986e-18
#> 9 10
#> AIC(n) -4.040127e+01 -4.045800e+01
#> HQ(n) -3.904778e+01 -3.895819e+01
#> SC(n) -3.706976e+01 -3.676633e+01
#> FPE(n) 3.054176e-18 2.964556e-18
#>
#>
#> Lag K to use for the VAR Model: 2
#> Printing summary of the VAR fit for the variable of interest: logGNP
#>
#> Call:
#> lm(formula = y ~ -1 + ., data = datamat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.0239854 -0.0051696 0.0001681 0.0056556 0.0235511
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> logM1.l1 7.899e-02 8.331e-02 0.948 0.345
#> logGNP.l1 1.046e+00 8.825e-02 11.848 <2e-16 ***
#> rs.l1 -2.031e-01 1.269e-01 -1.601 0.112
#> rl.l1 5.214e-01 2.657e-01 1.962 0.052 .
#> logM1.l2 -1.336e-01 8.324e-02 -1.605 0.111
#> logGNP.l2 2.233e-03 9.147e-02 0.024 0.981
#> rs.l2 -2.120e-01 1.372e-01 -1.545 0.125
#> rl.l2 -3.938e-01 2.595e-01 -1.517 0.132
#> trend -1.541e-04 9.498e-05 -1.623 0.107
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.008618 on 125 degrees of freedom
#> Multiple R-squared: 1, Adjusted R-squared: 1
#> F-statistic: 1.217e+07 on 9 and 125 DF, p-value: < 2.2e-16
#>
#>
#> Model: BIC Both
#> Trend type: both
#> Seasonality:
#> VARselect Object:
#> $selection
#> AIC(n) HQ(n) SC(n) FPE(n)
#> 10 2 2 10
#>
#> $criteria
#> 1 2 3 4
#> AIC(n) -3.982007e+01 -4.053699e+01 -4.057493e+01 -4.052772e+01
#> HQ(n) -3.960059e+01 -4.017118e+01 -4.006280e+01 -3.986926e+01
#> SC(n) -3.927983e+01 -3.963658e+01 -3.931436e+01 -3.890698e+01
#> FPE(n) 5.087308e-18 2.486513e-18 2.399570e-18 2.526125e-18
#> 5 6 7 8
#> AIC(n) -4.047172e+01 -4.050251e+01 -4.042691e+01 -4.048431e+01
#> HQ(n) -3.966695e+01 -3.955141e+01 -3.932949e+01 -3.924056e+01
#> SC(n) -3.849083e+01 -3.816145e+01 -3.772569e+01 -3.742293e+01
#> FPE(n) 2.689264e-18 2.632861e-18 2.877408e-18 2.764981e-18
#> 9 10
#> AIC(n) -4.055474e+01 -4.072060e+01
#> HQ(n) -3.916467e+01 -3.918421e+01
#> SC(n) -3.713319e+01 -3.693889e+01
#> FPE(n) 2.635876e-18 2.297582e-18
#>
#>
#> Lag K to use for the VAR Model: 2
#> Printing summary of the VAR fit for the variable of interest: logGNP
#>
#> Call:
#> lm(formula = y ~ -1 + ., data = datamat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.0242796 -0.0050814 0.0001488 0.0054701 0.0239546
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> logM1.l1 7.934e-02 8.356e-02 0.950 0.3442
#> logGNP.l1 1.045e+00 8.852e-02 11.802 <2e-16 ***
#> rs.l1 -1.804e-01 1.344e-01 -1.342 0.1819
#> rl.l1 4.791e-01 2.782e-01 1.722 0.0875 .
#> logM1.l2 -1.348e-01 8.351e-02 -1.614 0.1090
#> logGNP.l2 -1.170e-02 9.546e-02 -0.123 0.9026
#> rs.l2 -1.989e-01 1.398e-01 -1.423 0.1573
#> rl.l2 -4.136e-01 2.630e-01 -1.573 0.1183
#> const 1.135e-01 2.150e-01 0.528 0.5985
#> trend -2.116e-05 2.693e-04 -0.079 0.9375
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.008643 on 124 degrees of freedom
#> Multiple R-squared: 0.9992, Adjusted R-squared: 0.9991
#> F-statistic: 1.734e+04 on 9 and 124 DF, p-value: < 2.2e-16mdl_build$summarize_build() #> # A tibble: 10 x 9 #> Model select trend_type season p SigVar OriginalVar Lag MaxLag #>#> 1 AIC None aic none 0 3 logGNP~ logGNP -1 -1 #> 2 AIC Tre~ aic trend 0 3 logGNP~ logGNP -1 -1 #> 3 AIC Tre~ aic trend 0 3 rs.l1 rs -1 -1 #> 4 AIC Tre~ aic trend 0 3 rl.l1 rl -1 -1 #> 5 AIC Tre~ aic trend 0 3 trend trend 0 0 #> 6 AIC Both aic both 0 10 logGNP~ logGNP -1 -1 #> 7 AIC Both aic both 0 10 logM1.~ logM1 -3 -3 #> 8 BIC None bic none 0 2 logGNP~ logGNP -1 -1 #> 9 BIC Tre~ bic trend 0 2 logGNP~ logGNP -1 -1 #> 10 BIC Both bic both 0 2 logGNP~ logGNP -1 -1
mdl_build$get_recommendations() #> # A tibble: 6 x 7 #> Model trend_type season num_sig_vars lag_to_use vars_to_use season_to_use #>#> 1 AIC ~ both 0 2 3 logGNP,log~ 0 #> 2 AIC ~ none 0 1 1 logGNP 0 #> 3 AIC ~ trend 0 3 1 logGNP,rs,~ 0 #> 4 BIC ~ both 0 1 1 logGNP 0 #> 5 BIC ~ none 0 1 1 logGNP 0 #> 6 BIC ~ trend 0 1 1 logGNP 0
mdl_build$build_recommended_models() #> #> Model: AIC Both - R #> Trend type: both #> Seasonality: #> #> Printing summary of the VAR fit for the variable of interest: logGNP #> Call: #> lm(formula = y ~ -1 + ., data = datamat) #> #> Residuals: #> Min 1Q Median 3Q Max #> -0.023681 -0.006032 0.000158 0.006629 0.025152 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> logGNP.l1 1.1106065 0.0895101 12.408 <2e-16 *** #> logM1.l1 0.1854351 0.0807917 2.295 0.0234 * #> logGNP.l2 -0.0586080 0.1358634 -0.431 0.6669 #> logM1.l2 -0.0559067 0.1432458 -0.390 0.6970 #> logGNP.l3 -0.0881621 0.0886598 -0.994 0.3220 #> logM1.l3 -0.1204991 0.0933103 -1.291 0.1990 #> const 0.2136736 0.1659021 1.288 0.2001 #> trend 0.0002403 0.0002226 1.079 0.2825 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 0.009169 on 125 degrees of freedom #> Multiple R-squared: 0.9991, Adjusted R-squared: 0.999 #> F-statistic: 1.932e+04 on 7 and 125 DF, p-value: < 2.2e-16 #> #> #> Model: AIC None - R #> Trend type: none #> Seasonality: #> Warning in mdl_build$build_recommended_models(): This recommendation is #> to use just the variable of interest (dependent variable) to model the #> time series, hence this model will not be built. Please use a univariate #> approach to model this separately. #> #> Model: AIC Trend - R #> Trend type: trend #> Seasonality: #> #> Printing summary of the VAR fit for the variable of interest: logGNP #> Call: #> lm(formula = y ~ -1 + ., data = datamat) #> #> Residuals: #> Min 1Q Median 3Q Max #> -0.0265848 -0.0059894 -0.0002544 0.0059723 0.0253531 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> logGNP.l1 1.001e+00 2.797e-04 3580.480 < 2e-16 *** #> rs.l1 -3.124e-01 6.648e-02 -4.699 6.51e-06 *** #> rl.l1 1.678e-01 1.018e-01 1.648 0.102 #> trend 6.039e-05 4.811e-05 1.255 0.212 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 0.009185 on 131 degrees of freedom #> Multiple R-squared: 1, Adjusted R-squared: 1 #> F-statistic: 2.427e+07 on 4 and 131 DF, p-value: < 2.2e-16 #> #> #> Model: BIC Both - R #> Trend type: both #> Seasonality: #> Warning in mdl_build$build_recommended_models(): This recommendation is #> to use just the variable of interest (dependent variable) to model the #> time series, hence this model will not be built. Please use a univariate #> approach to model this separately. #> #> Model: BIC None - R #> Trend type: none #> Seasonality: #> Warning in mdl_build$build_recommended_models(): This recommendation is #> to use just the variable of interest (dependent variable) to model the #> time series, hence this model will not be built. Please use a univariate #> approach to model this separately. #> #> Model: BIC Trend - R #> Trend type: trend #> Seasonality: #> Warning in mdl_build$build_recommended_models(): This recommendation is #> to use just the variable of interest (dependent variable) to model the #> time series, hence this model will not be built. Please use a univariate #> approach to model this separately.
lag.max = 10
models = list("AIC Trend" = list(select = "aic", trend_type = "trend", season = 3, lag.max = lag.max),
"BIC Trend" = list(select = "bic", trend_type = "trend", season = 4, lag.max = lag.max)
)
var_interest = 'logGNP'mdl_build = ModelBuildMultivariateVAR$new(data = data, var_interest = var_interest,
mdl_list = models, verbose = 1)
#>
#> Model: AIC Trend
#> Trend type: trend
#> Seasonality: 3
#> VARselect Object:
#> $selection
#> AIC(n) HQ(n) SC(n) FPE(n)
#> 10 2 2 3
#>
#> $criteria
#> 1 2 3 4
#> AIC(n) -3.969330e+01 -4.035727e+01 -4.040031e+01 -4.032908e+01
#> HQ(n) -3.943724e+01 -3.995488e+01 -3.985160e+01 -3.963405e+01
#> SC(n) -3.906302e+01 -3.936682e+01 -3.904970e+01 -3.861831e+01
#> FPE(n) 5.775851e-18 2.977388e-18 2.859832e-18 3.085515e-18
#> 5 6 7 8
#> AIC(n) -4.031044e+01 -4.038497e+01 -4.030034e+01 -4.034316e+01
#> HQ(n) -3.946908e+01 -3.939729e+01 -3.916634e+01 -3.906284e+01
#> SC(n) -3.823950e+01 -3.795387e+01 -3.750908e+01 -3.719174e+01
#> FPE(n) 3.166565e-18 2.969970e-18 3.278585e-18 3.200558e-18
#> 9 10
#> AIC(n) -4.036929e+01 -4.043180e+01
#> HQ(n) -3.894264e+01 -3.885882e+01
#> SC(n) -3.685770e+01 -3.656005e+01
#> FPE(n) 3.193768e-18 3.091975e-18
#>
#>
#> Lag K to use for the VAR Model: 10
#> Printing summary of the VAR fit for the variable of interest: logGNP
#>
#> Call:
#> lm(formula = y ~ -1 + ., data = datamat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.0219594 -0.0045565 -0.0002284 0.0045124 0.0183180
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> logM1.l1 0.0030343 0.1449682 0.021 0.9834
#> logGNP.l1 0.9585307 0.1180966 8.116 3.81e-12 ***
#> rs.l1 -0.2037610 0.1750410 -1.164 0.2477
#> rl.l1 0.3893208 0.3335936 1.167 0.2465
#> logM1.l2 0.3078885 0.2346990 1.312 0.1932
#> logGNP.l2 -0.0249116 0.1632539 -0.153 0.8791
#> rs.l2 -0.1407506 0.2799097 -0.503 0.6164
#> rl.l2 -0.3660114 0.4227902 -0.866 0.3891
#> logM1.l3 -0.5335092 0.2332442 -2.287 0.0247 *
#> logGNP.l3 -0.1305560 0.1575116 -0.829 0.4096
#> rs.l3 0.0528748 0.3082302 0.172 0.8642
#> rl.l3 0.6726379 0.4432913 1.517 0.1330
#> logM1.l4 0.1153536 0.2461606 0.469 0.6406
#> logGNP.l4 0.0739205 0.1594700 0.464 0.6442
#> rs.l4 -0.3781166 0.3054014 -1.238 0.2192
#> rl.l4 -0.3148241 0.4547657 -0.692 0.4907
#> logM1.l5 0.1028368 0.2550076 0.403 0.6878
#> logGNP.l5 0.1069698 0.1600115 0.669 0.5057
#> rs.l5 -0.1454852 0.2956540 -0.492 0.6240
#> rl.l5 -0.2220033 0.4432610 -0.501 0.6178
#> logM1.l6 -0.0006586 0.2553319 -0.003 0.9979
#> logGNP.l6 0.1762698 0.1599435 1.102 0.2736
#> rs.l6 0.2793984 0.2797667 0.999 0.3209
#> rl.l6 -0.1746437 0.4532412 -0.385 0.7010
#> logM1.l7 -0.1460863 0.2483317 -0.588 0.5579
#> logGNP.l7 -0.0072981 0.1604371 -0.045 0.9638
#> rs.l7 -0.3634013 0.2746583 -1.323 0.1894
#> rl.l7 0.2895620 0.4969257 0.583 0.5617
#> logM1.l8 0.1719437 0.2542730 0.676 0.5008
#> logGNP.l8 -0.1209648 0.1606688 -0.753 0.4537
#> rs.l8 0.3745678 0.2804824 1.335 0.1854
#> rl.l8 -0.5389801 0.4976315 -1.083 0.2819
#> logM1.l9 -0.0415393 0.2463542 -0.169 0.8665
#> logGNP.l9 0.0530109 0.1521236 0.348 0.7284
#> rs.l9 0.0645883 0.2480657 0.260 0.7952
#> rl.l9 -0.0933852 0.5030994 -0.186 0.8532
#> logM1.l10 -0.1306522 0.1496646 -0.873 0.3852
#> logGNP.l10 0.0471835 0.1174385 0.402 0.6889
#> rs.l10 0.1377059 0.1934182 0.712 0.4785
#> rl.l10 -0.0262528 0.3938979 -0.067 0.9470
#> trend -0.0004566 0.0002018 -2.262 0.0263 *
#> sd1 0.0005344 0.0020861 0.256 0.7984
#> sd2 -0.0004906 0.0020775 -0.236 0.8139
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.008634 on 83 degrees of freedom
#> Multiple R-squared: 1, Adjusted R-squared: 1
#> F-statistic: 2.405e+06 on 43 and 83 DF, p-value: < 2.2e-16
#> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [3].
#>
#> Model: BIC Trend
#> Trend type: trend
#> Seasonality: 4
#> VARselect Object:
#> $selection
#> AIC(n) HQ(n) SC(n) FPE(n)
#> 10 2 2 3
#>
#> $criteria
#> 1 2 3 4
#> AIC(n) -3.966241e+01 -4.039732e+01 -4.043410e+01 -4.036456e+01
#> HQ(n) -3.936977e+01 -3.995835e+01 -3.984881e+01 -3.963295e+01
#> SC(n) -3.894209e+01 -3.931683e+01 -3.899345e+01 -3.856375e+01
#> FPE(n) 5.958409e-18 2.862027e-18 2.767496e-18 2.982605e-18
#> 5 6 7 8
#> AIC(n) -4.034440e+01 -4.043169e+01 -4.035008e+01 -4.037674e+01
#> HQ(n) -3.946646e+01 -3.940743e+01 -3.917950e+01 -3.905983e+01
#> SC(n) -3.818342e+01 -3.791055e+01 -3.746878e+01 -3.713528e+01
#> FPE(n) 3.067848e-18 2.843418e-18 3.132730e-18 3.111869e-18
#> 9 10
#> AIC(n) -4.040028e+01 -4.048840e+01
#> HQ(n) -3.893705e+01 -3.887885e+01
#> SC(n) -3.679865e+01 -3.652661e+01
#> FPE(n) 3.117816e-18 2.947034e-18
#>
#>
#> Lag K to use for the VAR Model: 2
#> Printing summary of the VAR fit for the variable of interest: logGNP
#>
#> Call:
#> lm(formula = y ~ -1 + ., data = datamat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.0265294 -0.0045724 0.0001322 0.0054340 0.0208006
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> logM1.l1 8.479e-02 8.209e-02 1.033 0.30372
#> logGNP.l1 1.065e+00 8.828e-02 12.067 < 2e-16 ***
#> rs.l1 -2.231e-01 1.266e-01 -1.762 0.08055 .
#> rl.l1 5.949e-01 2.630e-01 2.262 0.02548 *
#> logM1.l2 -1.389e-01 8.196e-02 -1.695 0.09270 .
#> logGNP.l2 -1.795e-02 9.150e-02 -0.196 0.84479
#> rs.l2 -1.949e-01 1.366e-01 -1.427 0.15616
#> rl.l2 -4.603e-01 2.567e-01 -1.793 0.07545 .
#> trend -1.533e-04 9.316e-05 -1.646 0.10230
#> sd1 5.876e-03 2.083e-03 2.821 0.00559 **
#> sd2 2.113e-03 2.097e-03 1.008 0.31564
#> sd3 2.888e-03 2.076e-03 1.391 0.16676
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.008446 on 122 degrees of freedom
#> Multiple R-squared: 1, Adjusted R-squared: 1
#> F-statistic: 9.505e+06 on 12 and 122 DF, p-value: < 2.2e-16
#> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [3].mdl_build$summarize_build() #> # A tibble: 6 x 9 #> Model select trend_type season p SigVar OriginalVar Lag MaxLag #>#> 1 AIC Tre~ aic trend 3 10 logGNP.~ logGNP -1 -1 #> 2 AIC Tre~ aic trend 3 10 logM1.l3 logM1 -3 -3 #> 3 AIC Tre~ aic trend 3 10 trend trend 0 0 #> 4 BIC Tre~ bic trend 4 2 logGNP.~ logGNP -1 -1 #> 5 BIC Tre~ bic trend 4 2 rl.l1 rl -1 -1 #> 6 BIC Tre~ bic trend 4 2 sd1 sd1 0 0
mdl_build$build_recommended_models() #> #> Model: AIC Trend - R #> Trend type: trend #> Seasonality: #> #> Printing summary of the VAR fit for the variable of interest: logGNP #> Call: #> lm(formula = y ~ -1 + ., data = datamat) #> #> Residuals: #> Min 1Q Median 3Q Max #> -0.0226368 -0.0061161 -0.0001477 0.0063173 0.0243018 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> logGNP.l1 1.119e+00 8.953e-02 12.495 <2e-16 *** #> logM1.l1 2.059e-01 7.943e-02 2.592 0.0107 * #> logGNP.l2 -5.941e-02 1.362e-01 -0.436 0.6635 #> logM1.l2 -5.565e-02 1.436e-01 -0.387 0.6991 #> logGNP.l3 -6.099e-02 8.634e-02 -0.706 0.4812 #> logM1.l3 -1.469e-01 9.127e-02 -1.610 0.1100 #> trend -1.748e-05 9.770e-05 -0.179 0.8583 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 0.009193 on 126 degrees of freedom #> Multiple R-squared: 1, Adjusted R-squared: 1 #> F-statistic: 1.366e+07 on 7 and 126 DF, p-value: < 2.2e-16 #> #> #> Model: BIC Trend - R #> Trend type: trend #> Seasonality: 4 #> #> Printing summary of the VAR fit for the variable of interest: logGNP #> Call: #> lm(formula = y ~ -1 + ., data = datamat) #> #> Residuals: #> Min 1Q Median 3Q Max #> -0.0308053 -0.0062412 0.0006855 0.0059003 0.0228881 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> logGNP.l1 1.002e+00 2.890e-04 3466.299 < 2e-16 *** #> rl.l1 -2.086e-01 6.712e-02 -3.107 0.00232 ** #> trend 1.176e-04 4.995e-05 2.355 0.02004 * #> sd1 4.450e-03 2.411e-03 1.846 0.06723 . #> sd2 1.144e-03 2.393e-03 0.478 0.63328 #> sd3 2.254e-03 2.393e-03 0.942 0.34796 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 0.009865 on 129 degrees of freedom #> Multiple R-squared: 1, Adjusted R-squared: 1 #> F-statistic: 1.403e+07 on 6 and 129 DF, p-value: < 2.2e-16
# Get only recommended model models = mdl_build$get_final_models(subset = 'r') print(names(models)) #> [1] "AIC Trend - R" "BIC Trend - R"
# Get all models models = mdl_build$get_final_models(subset = 'a') print(names(models)) #> [1] "AIC Trend" "BIC Trend" "AIC Trend - R" "BIC Trend - R"