This function computes Geweke's convergence diagnostic for Markov chain Monte Carlo
(MCMC) output obtained from Bayesian estimation of multivariate TAR models. It is a
wrapper around geweke.diag() that applies the diagnostic to the posterior chains
returned by a call to mtar().
Arguments
- x
An object of class
mtarreturned by the functionmtar().- frac1
A numeric value in \((0,1)\) specifying the fraction of the initial part of each chain to be used in the diagnostic.
- frac2
A numeric value in \((0,1)\) specifying the fraction of the final part of each chain to be used in the diagnostic.
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)
geweke_diagTAR(fit1)
#>
#> Fraction in 1st window = 0.1
#>
#> Fraction in 2nd window = 0.5
#>
#> Thresholds:
#>
#> Threshold.1 0.11356
#> Threshold.2 -2.70536
#>
#>
#> Autoregressive coefficients:
#> Regime 1 Regime 2 Regime 3
#> COLCAP:(Intercept) 1.745416 -0.30833 3.375604
#> COLCAP:COLCAP.lag(1) 1.038377 -0.66840 -0.270495
#> COLCAP:COLCAP.lag(2) -0.656604
#> COLCAP:BOVESPA.lag(1) -0.064165 0.12503 1.845537
#> COLCAP:BOVESPA.lag(2) 1.567525
#> BOVESPA:(Intercept) -0.214358 -0.20241 -0.755315
#> BOVESPA:COLCAP.lag(1) -0.093370 -1.40943 0.033879
#> BOVESPA:COLCAP.lag(2) -0.263949
#> BOVESPA:BOVESPA.lag(1) -2.266093 1.44166 0.206484
#> BOVESPA:BOVESPA.lag(2) 0.203798
#>
#>
#> Scale parameter:
#> Regime 1 Regime 2 Regime 3
#> COLCAP.COLCAP -1.6540 -3.6273 -1.47561
#> COLCAP.BOVESPA -2.0927 -3.0546 -2.26107
#> BOVESPA.BOVESPA -4.0079 -4.2834 -0.99057
#>
#>
#> Extra parameter:
#>
#> nu -1.9409
###### 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)
geweke_diagTAR(fit2)
#>
#> Fraction in 1st window = 0.1
#>
#> Fraction in 2nd window = 0.5
#>
#> Thresholds:
#>
#> Threshold.1 -6.6118
#> Threshold.2 -3.2370
#>
#>
#> Autoregressive coefficients:
#> Regime 1 Regime 2 Regime 3
#> Bedon:(Intercept) 0.609746 -0.194415 -0.068429
#> Bedon:Bedon.lag(1) 0.259699 -0.930640 1.458702
#> Bedon:Bedon.lag(2) -0.457895 -0.077164 -1.394193
#> Bedon:Bedon.lag(3) 1.008299 -0.985041 -1.757549
#> Bedon:Bedon.lag(4) -0.045598 1.998524 0.674293
#> Bedon:Bedon.lag(5) 0.322967 -1.137978 -0.319331
#> Bedon:LaPlata.lag(1) -3.763417 2.155283 -0.047187
#> Bedon:LaPlata.lag(2) 5.437253 0.003872 -0.678272
#> Bedon:LaPlata.lag(3) -2.152364 0.906505 0.071907
#> Bedon:LaPlata.lag(4) 0.397025 -0.982121 0.818494
#> Bedon:LaPlata.lag(5) 0.693351 -1.374299 -0.495198
#> LaPlata:(Intercept) 1.519028 0.730821 -1.933477
#> LaPlata:Bedon.lag(1) -0.350597 -0.802083 -0.070297
#> LaPlata:Bedon.lag(2) 0.174449 -1.138485 0.959153
#> LaPlata:Bedon.lag(3) -0.248497 0.826379 1.056641
#> LaPlata:Bedon.lag(4) -0.657543 1.880109 -0.250346
#> LaPlata:Bedon.lag(5) 0.498356 -0.702740 -1.698729
#> LaPlata:LaPlata.lag(1) -2.534480 1.804981 1.132650
#> LaPlata:LaPlata.lag(2) 2.234200 0.373357 -0.788856
#> LaPlata:LaPlata.lag(3) 0.422239 0.670537 -0.321663
#> LaPlata:LaPlata.lag(4) -1.506449 0.817669 0.025015
#> LaPlata:LaPlata.lag(5) 1.493079 -2.927121 0.329010
#>
#>
#> Scale parameter:
#> Regime 1 Regime 2 Regime 3
#> Bedon.Bedon -0.72568 0.51171 2.6814
#> Bedon.LaPlata -0.59744 -0.26023 1.7902
#> LaPlata.LaPlata 0.64288 0.52904 1.2831
###### 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")
geweke_diagTAR(fit3)
#>
#> Fraction in 1st window = 0.1
#>
#> Fraction in 2nd window = 0.5
#>
#> Thresholds:
#>
#> threshold -1.1574
#>
#>
#> Autoregressive coefficients:
#> Regime 1 Regime 2
#> Jokulsa:(Intercept) -0.823862 0.6109322
#> Jokulsa:Jokulsa.lag( 1) 1.094004 1.0780484
#> Jokulsa:Jokulsa.lag( 2) 0.277770 -1.0066175
#> Jokulsa:Jokulsa.lag( 3) -0.351499 1.0995278
#> Jokulsa:Jokulsa.lag( 4) -0.371079 -0.1728502
#> Jokulsa:Jokulsa.lag( 5) -0.287541 -0.2688228
#> Jokulsa:Jokulsa.lag( 6) -0.349590 -0.0071128
#> Jokulsa:Jokulsa.lag( 7) 0.925982 -0.7064158
#> Jokulsa:Jokulsa.lag( 8) -0.761811 1.4911937
#> Jokulsa:Jokulsa.lag( 9) -0.371536 -1.3739013
#> Jokulsa:Jokulsa.lag(10) 0.538585 1.8168860
#> Jokulsa:Jokulsa.lag(11) 0.421276 -2.3095396
#> Jokulsa:Jokulsa.lag(12) 0.034378 1.8729635
#> Jokulsa:Jokulsa.lag(13) 3.008883 -0.7532789
#> Jokulsa:Jokulsa.lag(14) -0.834252 0.5970718
#> Jokulsa:Jokulsa.lag(15) -1.858247 -0.9083479
#> Jokulsa:Vatnsdalsa.lag( 1) 2.936426 -2.0035680
#> Jokulsa:Vatnsdalsa.lag( 2) -2.126379 0.6507942
#> Jokulsa:Vatnsdalsa.lag( 3) -0.604717 -0.5851513
#> Jokulsa:Vatnsdalsa.lag( 4) 1.131303 -1.7071196
#> Jokulsa:Vatnsdalsa.lag( 5) -0.045075 1.4530296
#> Jokulsa:Vatnsdalsa.lag( 6) -1.108380 -1.8760778
#> Jokulsa:Vatnsdalsa.lag( 7) 1.398213 1.6066348
#> Jokulsa:Vatnsdalsa.lag( 8) -1.631388 -1.0361706
#> Jokulsa:Vatnsdalsa.lag( 9) 1.662547 0.8162786
#> Jokulsa:Vatnsdalsa.lag(10) 0.034633 -0.1567618
#> Jokulsa:Vatnsdalsa.lag(11) -2.153253 -0.7390121
#> Jokulsa:Vatnsdalsa.lag(12) -0.773510 3.0732424
#> Jokulsa:Vatnsdalsa.lag(13) -0.636791 -1.0879242
#> Jokulsa:Vatnsdalsa.lag(14) 0.558124 1.2203508
#> Jokulsa:Vatnsdalsa.lag(15) -0.677028 0.0057302
#> Jokulsa:Precipitation.lag(1) 0.284061 -0.8269849
#> Jokulsa:Precipitation.lag(2) -1.101944 0.8863884
#> Jokulsa:Precipitation.lag(3) 0.135629 -1.0898838
#> Jokulsa:Precipitation.lag(4) -0.710571 1.0781812
#> Jokulsa:Temperature.lag(1) -0.537702 0.3180306
#> Jokulsa:Temperature.lag(2) 1.370769 0.0144458
#> Vatnsdalsa:(Intercept) -3.160850 0.3567473
#> Vatnsdalsa:Jokulsa.lag( 1) 2.379660 -0.3912094
#> Vatnsdalsa:Jokulsa.lag( 2) 2.851764 0.3266082
#> Vatnsdalsa:Jokulsa.lag( 3) -5.677851 -0.3162113
#> Vatnsdalsa:Jokulsa.lag( 4) 0.542770 1.5621443
#> Vatnsdalsa:Jokulsa.lag( 5) 0.591480 0.2846014
#> Vatnsdalsa:Jokulsa.lag( 6) -0.122440 -1.6729661
#> Vatnsdalsa:Jokulsa.lag( 7) -1.003503 0.4233115
#> Vatnsdalsa:Jokulsa.lag( 8) 0.455903 -0.1289307
#> Vatnsdalsa:Jokulsa.lag( 9) -0.513075 1.1017728
#> Vatnsdalsa:Jokulsa.lag(10) 0.469645 0.4894130
#> Vatnsdalsa:Jokulsa.lag(11) 0.443964 -0.6360787
#> Vatnsdalsa:Jokulsa.lag(12) 0.033230 0.7120535
#> Vatnsdalsa:Jokulsa.lag(13) 0.728990 -0.5998214
#> Vatnsdalsa:Jokulsa.lag(14) -0.989275 -0.7326892
#> Vatnsdalsa:Jokulsa.lag(15) -0.462880 0.7901889
#> Vatnsdalsa:Vatnsdalsa.lag( 1) 3.748639 0.8593586
#> Vatnsdalsa:Vatnsdalsa.lag( 2) -3.704252 -0.8872188
#> Vatnsdalsa:Vatnsdalsa.lag( 3) -0.436174 -1.8864874
#> Vatnsdalsa:Vatnsdalsa.lag( 4) 1.974799 0.9302210
#> Vatnsdalsa:Vatnsdalsa.lag( 5) -0.784182 -3.3513613
#> Vatnsdalsa:Vatnsdalsa.lag( 6) -1.679059 2.1958604
#> Vatnsdalsa:Vatnsdalsa.lag( 7) 2.155702 -0.9034269
#> Vatnsdalsa:Vatnsdalsa.lag( 8) -0.753057 -2.2526170
#> Vatnsdalsa:Vatnsdalsa.lag( 9) 1.245222 1.3710749
#> Vatnsdalsa:Vatnsdalsa.lag(10) -0.812394 -0.6941822
#> Vatnsdalsa:Vatnsdalsa.lag(11) -2.040300 0.2606420
#> Vatnsdalsa:Vatnsdalsa.lag(12) -1.284146 1.3577733
#> Vatnsdalsa:Vatnsdalsa.lag(13) 0.806696 -0.7685607
#> Vatnsdalsa:Vatnsdalsa.lag(14) 0.079522 0.1060577
#> Vatnsdalsa:Vatnsdalsa.lag(15) 0.731686 -0.4813458
#> Vatnsdalsa:Precipitation.lag(1) -1.559372 0.8548126
#> Vatnsdalsa:Precipitation.lag(2) -0.942670 0.2470207
#> Vatnsdalsa:Precipitation.lag(3) 3.131694 -2.0528026
#> Vatnsdalsa:Precipitation.lag(4) 0.820011 1.1571862
#> Vatnsdalsa:Temperature.lag(1) -2.945585 3.4123432
#> Vatnsdalsa:Temperature.lag(2) 1.979369 -2.0061832
#>
#>
#> Scale parameter:
#> Regime 1 Regime 2
#> Jokulsa.Jokulsa -4.2318 2.1207
#> Jokulsa.Vatnsdalsa -2.3976 1.6985
#> Vatnsdalsa.Vatnsdalsa -2.9155 2.1476
#>
#>
#> Extra parameter:
#>
#> nu -1.06
###### 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")
geweke_diagTAR(fit4)
#>
#> Fraction in 1st window = 0.1
#>
#> Fraction in 2nd window = 0.5
#>
#> Thresholds:
#>
#> threshold 0.41314
#>
#>
#> Autoregressive coefficients:
#> Regime 1 Regime 2
#> CCR:(Intercept) 2.14097 0.91414
#> CCR:CCR.lag(1) 0.45316 0.25510
#> CCR:CCR.lag(2) -2.38108 1.97836
#> CCR:CCR.lag(3) -1.41131 -0.89380
#> CCR:dVIX.lag(1) -1.68308 2.92253
#> CCR:dVIX.lag(2) -1.02757 -2.89144
#> CCR:dVIX.lag(3) 0.43897 0.75047
#>
#>
#> Scale parameter:
#> Regime 1 Regime 2
#> CCR.CCR 1.333 0.85975
#>
#>
#> Extra parameter:
#>
#> nu 1.1756
# }