This function computes the effective sample size, adjusted
for autocorrelation, of Markov chain Monte Carlo (MCMC) output obtained
from the Bayesian estimation of multivariate TAR models. It serves as a
wrapper around effectiveSize(), applying this function to the
posterior chains returned by mtar().
Arguments
- x
An object of class
mtarproduced bymtar().
Examples
# \donttest{
###### Example 1: Returns of the closing prices of three financial indexes
data(returns)
fit1 <- mtar(~ COLCAP + BOVESPA | SP500, data=returns, row.names=Date,
subset={Date<="2015-12-07"}, dist="Student-t",
ars=ars(nregim=3,p=c(1,1,2)), n.burnin=100, n.sim=200,
n.thin=2)
effectiveSize_TAR(fit1)
#> Thresholds:
#>
#> Threshold.1 9.5592
#> Threshold.2 1.9949
#>
#>
#> Autoregressive coefficients:
#> Regime 1 Regime 2 Regime 3
#> COLCAP:(Intercept) 200.00 154.80 200.00
#> COLCAP:COLCAP.lag(1) 185.86 200.00 200.00
#> COLCAP:COLCAP.lag(2) 170.94
#> COLCAP:BOVESPA.lag(1) 200.00 200.00 161.76
#> COLCAP:BOVESPA.lag(2) 200.00
#> BOVESPA:(Intercept) 200.00 200.00 107.85
#> BOVESPA:COLCAP.lag(1) 141.02 160.67 200.00
#> BOVESPA:COLCAP.lag(2) 350.08
#> BOVESPA:BOVESPA.lag(1) 200.00 153.57 149.84
#> BOVESPA:BOVESPA.lag(2) 160.65
#>
#>
#> Scale parameter:
#> Regime 1 Regime 2 Regime 3
#> COLCAP.COLCAP 103.87 106.84 200.00
#> COLCAP.BOVESPA 109.34 200.00 136.30
#> BOVESPA.BOVESPA 105.00 103.23 146.95
#>
#>
#> Extra parameter:
#>
#> nu 25.007
###### Example 2: Rainfall and two river flows in Colombia
data(riverflows)
fit2 <- mtar(~ Bedon + LaPlata | Rainfall, data=riverflows, row.names=Date,
subset={Date<="2009-02-13"}, dist="Laplace",
ars=ars(nregim=3,p=5), n.burnin=100, n.sim=200, n.thin=2)
effectiveSize_TAR(fit2)
#> Thresholds:
#>
#> Threshold.1 2.1775
#> Threshold.2 3.7982
#>
#>
#> Autoregressive coefficients:
#> Regime 1 Regime 2 Regime 3
#> Bedon:(Intercept) 131.632 73.907 69.621
#> Bedon:Bedon.lag(1) 45.747 87.809 65.008
#> Bedon:Bedon.lag(2) 106.363 65.598 104.533
#> Bedon:Bedon.lag(3) 94.695 73.122 112.791
#> Bedon:Bedon.lag(4) 63.189 52.081 90.252
#> Bedon:Bedon.lag(5) 119.323 82.708 85.234
#> Bedon:LaPlata.lag(1) 111.322 152.102 83.797
#> Bedon:LaPlata.lag(2) 122.614 78.559 108.676
#> Bedon:LaPlata.lag(3) 99.508 69.338 100.341
#> Bedon:LaPlata.lag(4) 110.962 95.440 94.710
#> Bedon:LaPlata.lag(5) 78.839 68.139 139.877
#> LaPlata:(Intercept) 86.705 63.164 84.790
#> LaPlata:Bedon.lag(1) 98.147 88.650 83.246
#> LaPlata:Bedon.lag(2) 119.332 80.562 131.717
#> LaPlata:Bedon.lag(3) 140.275 104.014 158.235
#> LaPlata:Bedon.lag(4) 107.452 82.790 87.551
#> LaPlata:Bedon.lag(5) 92.370 83.929 115.420
#> LaPlata:LaPlata.lag(1) 99.631 125.995 170.664
#> LaPlata:LaPlata.lag(2) 109.163 80.270 90.364
#> LaPlata:LaPlata.lag(3) 113.339 92.114 164.834
#> LaPlata:LaPlata.lag(4) 109.337 83.272 108.607
#> LaPlata:LaPlata.lag(5) 81.994 89.997 129.032
#>
#>
#> Scale parameter:
#> Regime 1 Regime 2 Regime 3
#> Bedon.Bedon 200.00 128.52 112.22
#> Bedon.LaPlata 159.83 145.19 136.60
#> LaPlata.LaPlata 116.37 401.35 133.43
###### Example 3: Temperature, precipitation, and two river flows in Iceland
data(iceland.rf)
fit3 <- mtar(~ Jokulsa + Vatnsdalsa | Temperature | Precipitation,
data=iceland.rf, subset={Date<="1974-11-06"}, row.names=Date,
ars=ars(nregim=2,p=15,q=4,d=2), n.burnin=100, n.sim=200,
n.thin=2, dist="Slash")
effectiveSize_TAR(fit3)
#> Thresholds:
#>
#> threshold 3.9387
#>
#>
#> Autoregressive coefficients:
#> Regime 1 Regime 2
#> Jokulsa:(Intercept) 94.367 100.200
#> Jokulsa:Jokulsa.lag( 1) 42.852 55.797
#> Jokulsa:Jokulsa.lag( 2) 64.175 47.173
#> Jokulsa:Jokulsa.lag( 3) 70.163 86.668
#> Jokulsa:Jokulsa.lag( 4) 35.747 104.423
#> Jokulsa:Jokulsa.lag( 5) 31.512 116.652
#> Jokulsa:Jokulsa.lag( 6) 18.939 140.411
#> Jokulsa:Jokulsa.lag( 7) 29.574 78.519
#> Jokulsa:Jokulsa.lag( 8) 32.732 136.205
#> Jokulsa:Jokulsa.lag( 9) 66.745 151.320
#> Jokulsa:Jokulsa.lag(10) 200.000 271.971
#> Jokulsa:Jokulsa.lag(11) 125.836 114.527
#> Jokulsa:Jokulsa.lag(12) 114.003 144.682
#> Jokulsa:Jokulsa.lag(13) 40.341 101.797
#> Jokulsa:Jokulsa.lag(14) 75.769 31.715
#> Jokulsa:Jokulsa.lag(15) 12.489 108.122
#> Jokulsa:Vatnsdalsa.lag( 1) 64.833 73.252
#> Jokulsa:Vatnsdalsa.lag( 2) 57.734 79.233
#> Jokulsa:Vatnsdalsa.lag( 3) 30.584 61.819
#> Jokulsa:Vatnsdalsa.lag( 4) 34.356 177.847
#> Jokulsa:Vatnsdalsa.lag( 5) 110.323 51.043
#> Jokulsa:Vatnsdalsa.lag( 6) 85.861 38.094
#> Jokulsa:Vatnsdalsa.lag( 7) 103.700 66.691
#> Jokulsa:Vatnsdalsa.lag( 8) 131.287 99.230
#> Jokulsa:Vatnsdalsa.lag( 9) 138.581 67.657
#> Jokulsa:Vatnsdalsa.lag(10) 158.157 50.973
#> Jokulsa:Vatnsdalsa.lag(11) 200.000 47.377
#> Jokulsa:Vatnsdalsa.lag(12) 200.000 50.928
#> Jokulsa:Vatnsdalsa.lag(13) 126.050 41.205
#> Jokulsa:Vatnsdalsa.lag(14) 118.813 79.054
#> Jokulsa:Vatnsdalsa.lag(15) 115.744 76.437
#> Jokulsa:Precipitation.lag(1) 95.639 106.574
#> Jokulsa:Precipitation.lag(2) 200.000 86.488
#> Jokulsa:Precipitation.lag(3) 156.916 158.826
#> Jokulsa:Precipitation.lag(4) 64.847 97.556
#> Jokulsa:Temperature.lag(1) 330.231 80.202
#> Jokulsa:Temperature.lag(2) 345.012 63.792
#> Vatnsdalsa:(Intercept) 111.818 86.962
#> Vatnsdalsa:Jokulsa.lag( 1) 86.702 145.821
#> Vatnsdalsa:Jokulsa.lag( 2) 38.782 153.123
#> Vatnsdalsa:Jokulsa.lag( 3) 108.596 146.713
#> Vatnsdalsa:Jokulsa.lag( 4) 94.824 149.421
#> Vatnsdalsa:Jokulsa.lag( 5) 100.900 103.723
#> Vatnsdalsa:Jokulsa.lag( 6) 69.261 217.992
#> Vatnsdalsa:Jokulsa.lag( 7) 22.207 113.211
#> Vatnsdalsa:Jokulsa.lag( 8) 61.685 131.621
#> Vatnsdalsa:Jokulsa.lag( 9) 125.051 116.786
#> Vatnsdalsa:Jokulsa.lag(10) 84.424 107.136
#> Vatnsdalsa:Jokulsa.lag(11) 91.298 130.204
#> Vatnsdalsa:Jokulsa.lag(12) 124.199 112.888
#> Vatnsdalsa:Jokulsa.lag(13) 105.712 103.219
#> Vatnsdalsa:Jokulsa.lag(14) 91.513 153.882
#> Vatnsdalsa:Jokulsa.lag(15) 84.091 101.509
#> Vatnsdalsa:Vatnsdalsa.lag( 1) 33.952 71.378
#> Vatnsdalsa:Vatnsdalsa.lag( 2) 56.436 42.485
#> Vatnsdalsa:Vatnsdalsa.lag( 3) 25.085 77.184
#> Vatnsdalsa:Vatnsdalsa.lag( 4) 29.254 78.553
#> Vatnsdalsa:Vatnsdalsa.lag( 5) 56.815 63.082
#> Vatnsdalsa:Vatnsdalsa.lag( 6) 125.360 42.795
#> Vatnsdalsa:Vatnsdalsa.lag( 7) 144.527 40.652
#> Vatnsdalsa:Vatnsdalsa.lag( 8) 200.000 44.292
#> Vatnsdalsa:Vatnsdalsa.lag( 9) 200.000 36.999
#> Vatnsdalsa:Vatnsdalsa.lag(10) 143.497 80.069
#> Vatnsdalsa:Vatnsdalsa.lag(11) 117.949 50.662
#> Vatnsdalsa:Vatnsdalsa.lag(12) 200.000 80.479
#> Vatnsdalsa:Vatnsdalsa.lag(13) 115.912 99.480
#> Vatnsdalsa:Vatnsdalsa.lag(14) 142.429 81.439
#> Vatnsdalsa:Vatnsdalsa.lag(15) 148.675 42.019
#> Vatnsdalsa:Precipitation.lag(1) 134.070 107.325
#> Vatnsdalsa:Precipitation.lag(2) 121.245 65.452
#> Vatnsdalsa:Precipitation.lag(3) 134.597 100.484
#> Vatnsdalsa:Precipitation.lag(4) 74.413 154.051
#> Vatnsdalsa:Temperature.lag(1) 125.790 93.908
#> Vatnsdalsa:Temperature.lag(2) 94.328 92.488
#>
#>
#> Scale parameter:
#> Regime 1 Regime 2
#> Jokulsa.Jokulsa 15.925 20.378
#> Jokulsa.Vatnsdalsa 41.806 58.128
#> Vatnsdalsa.Vatnsdalsa 35.704 21.968
#>
#>
#> Extra parameter:
#>
#> nu 41.925
###### Example 4: U.S. stock returns
data(US.returns)
fit4 <- mtar(~ CCR | dVIX, data=US.returns, subset={Date<="2025-11-28"},
row.names=Date, ars=ars(nregim=2,p=3,d=3), n.burnin=100,
n.sim=200, n.thin=2, dist="Student-t")
effectiveSize_TAR(fit4)
#> Thresholds:
#>
#> threshold 3.4869
#>
#>
#> Autoregressive coefficients:
#> Regime 1 Regime 2
#> CCR:(Intercept) 137.98 134.743
#> CCR:CCR.lag(1) 133.20 108.034
#> CCR:CCR.lag(2) 137.42 141.327
#> CCR:CCR.lag(3) 161.31 89.226
#> CCR:dVIX.lag(1) 125.78 200.000
#> CCR:dVIX.lag(2) 200.00 107.326
#> CCR:dVIX.lag(3) 196.72 85.211
#>
#>
#> Scale parameter:
#> Regime 1 Regime 2
#> CCR.CCR 63.198 105.26
#>
#>
#> Extra parameter:
#>
#> nu 33.028
# }