Highest Posterior Density intervals for objects of class mtar
Source: R/bayesians.R
HPDinterval.mtar.RdHighest Posterior Density intervals for objects of class mtar
Arguments
- obj
an object of class
mtargenerated by a call to the functionmtar().- prob
a numeric scalar in the interval \((0,1)\) giving the target probability content of the intervals. By default,
probis set to0.95.- ...
Optional additional arguments for methods. None are used at present.
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=1000, n.sim=2000,
n.thin=2, ssvs=TRUE)
coda::HPDinterval(fit1)
#>
#> Probability = 0.95
#>
#> Thresholds:
#> lower upper
#> Threshold.1 -0.0105420 -0.0049244
#> Threshold.2 0.0095292 0.0110304
#>
#>
#> Regime 1
#>
#>
#> Autoregressive coefficients:
#> lower upper
#> COLCAP:(Intercept) -0.0097865 -0.0046935
#> BOVESPA:(Intercept) -0.0175248 -0.0104906
#> COLCAP:COLCAP.lag(1) 0.0714910 0.4955079
#> BOVESPA:COLCAP.lag(1) -0.3362183 0.1521801
#> COLCAP:BOVESPA.lag(1) -0.0064880 0.2706587
#> BOVESPA:BOVESPA.lag(1) -0.0491111 0.3261144
#>
#>
#> Scale parameter:
#> lower upper
#> COLCAP.COLCAP 5.7862e-05 1.0095e-04
#> COLCAP.BOVESPA 1.9654e-05 5.9056e-05
#> BOVESPA.BOVESPA 1.0877e-04 1.8919e-04
#>
#>
#> Regime 2
#>
#>
#> Autoregressive coefficients:
#> lower upper
#> COLCAP:(Intercept) -0.00033615 0.00060734
#> BOVESPA:(Intercept) -0.00106372 0.00047654
#> COLCAP:COLCAP.lag(1) 0.00818074 0.11625723
#> BOVESPA:COLCAP.lag(1) -0.02070697 0.13605005
#> COLCAP:BOVESPA.lag(1) 0.04220101 0.11025610
#> BOVESPA:BOVESPA.lag(1) -0.09427521 0.01043800
#>
#>
#> Scale parameter:
#> lower upper
#> COLCAP.COLCAP 3.7111e-05 4.7470e-05
#> COLCAP.BOVESPA 9.7003e-06 1.8399e-05
#> BOVESPA.BOVESPA 8.0153e-05 1.0202e-04
#>
#>
#> Regime 3
#>
#>
#> Autoregressive coefficients:
#> lower upper
#> COLCAP:(Intercept) 0.0050632 0.0082017
#> BOVESPA:(Intercept) 0.0127336 0.0177791
#> COLCAP:COLCAP.lag(1) -0.0704008 0.2227840
#> BOVESPA:COLCAP.lag(1) -0.0635717 0.3455599
#> COLCAP:BOVESPA.lag(1) -0.1371853 0.0996818
#> BOVESPA:BOVESPA.lag(1) -0.3955939 -0.0604130
#> COLCAP:COLCAP.lag(2) -0.0755365 0.2143032
#> BOVESPA:COLCAP.lag(2) -0.2657331 0.1382123
#> COLCAP:BOVESPA.lag(2) -0.1589451 0.0413391
#> BOVESPA:BOVESPA.lag(2) -0.2036336 0.1165574
#>
#>
#> Scale parameter:
#> lower upper
#> COLCAP.COLCAP 4.3426e-05 7.5928e-05
#> COLCAP.BOVESPA 6.4743e-06 3.8754e-05
#> BOVESPA.BOVESPA 9.7007e-05 1.6814e-04
#>
#>
#> Extra parameter:
#> lower upper
#> nu 4.5431 7.3165
###### 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=1000, n.sim=2000, n.thin=2)
coda::HPDinterval(fit2)
#>
#> Probability = 0.95
#>
#> Thresholds:
#> lower upper
#> Threshold.1 3.0287 3.8155
#> Threshold.2 10.0000 10.0149
#>
#>
#> Regime 1
#>
#>
#> Autoregressive coefficients:
#> lower upper
#> Bedon:(Intercept) 1.108891 1.53216226
#> LaPlata:(Intercept) 2.860044 4.04402149
#> Bedon:Bedon.lag(1) 0.482920 0.63969522
#> LaPlata:Bedon.lag(1) -0.063305 0.34888959
#> Bedon:LaPlata.lag(1) 0.016203 0.07358646
#> LaPlata:LaPlata.lag(1) 0.554588 0.70845898
#> Bedon:Bedon.lag(2) -0.019105 0.11604248
#> LaPlata:Bedon.lag(2) -0.237660 0.13904147
#> Bedon:LaPlata.lag(2) -0.045098 0.00503449
#> LaPlata:LaPlata.lag(2) -0.136166 0.00093774
#> Bedon:Bedon.lag(3) -0.032472 0.09042262
#> LaPlata:Bedon.lag(3) -0.139796 0.16376737
#> Bedon:LaPlata.lag(3) -0.017717 0.02469308
#> LaPlata:LaPlata.lag(3) 0.012149 0.11990613
#> Bedon:Bedon.lag(4) -0.021334 0.10134620
#> LaPlata:Bedon.lag(4) -0.259139 0.05819055
#> Bedon:LaPlata.lag(4) -0.032318 0.00155077
#> LaPlata:LaPlata.lag(4) -0.045775 0.05922443
#> Bedon:Bedon.lag(5) 0.031486 0.13375885
#> LaPlata:Bedon.lag(5) 0.027727 0.27832650
#> Bedon:LaPlata.lag(5) -0.019542 0.00777224
#> LaPlata:LaPlata.lag(5) -0.017927 0.06704365
#>
#>
#> Scale parameter:
#> lower upper
#> Bedon.Bedon 0.26328 0.38948
#> Bedon.LaPlata 0.25262 0.48966
#> LaPlata.LaPlata 1.89676 2.77788
#>
#>
#> Regime 2
#>
#>
#> Autoregressive coefficients:
#> lower upper
#> Bedon:(Intercept) 1.3194596 2.916638
#> LaPlata:(Intercept) 4.8508130 8.910132
#> Bedon:Bedon.lag(1) 0.4953451 0.671637
#> LaPlata:Bedon.lag(1) -0.1002882 0.363021
#> Bedon:LaPlata.lag(1) -0.0077359 0.047793
#> LaPlata:LaPlata.lag(1) 0.4452416 0.599067
#> Bedon:Bedon.lag(2) -0.0290426 0.213174
#> LaPlata:Bedon.lag(2) -0.2339122 0.253258
#> Bedon:LaPlata.lag(2) -0.0543574 0.014471
#> LaPlata:LaPlata.lag(2) -0.0356174 0.108142
#> Bedon:Bedon.lag(3) -0.1465706 0.067465
#> LaPlata:Bedon.lag(3) -0.2800328 0.165803
#> Bedon:LaPlata.lag(3) -0.0396706 0.021091
#> LaPlata:LaPlata.lag(3) -0.0282009 0.111385
#> Bedon:Bedon.lag(4) -0.0039448 0.221469
#> LaPlata:Bedon.lag(4) -0.0148561 0.490533
#> Bedon:LaPlata.lag(4) -0.0293677 0.040315
#> LaPlata:LaPlata.lag(4) -0.1298770 0.038449
#> Bedon:Bedon.lag(5) -0.0629416 0.097919
#> LaPlata:Bedon.lag(5) -0.4876191 -0.092673
#> Bedon:LaPlata.lag(5) -0.0259730 0.031472
#> LaPlata:LaPlata.lag(5) 0.0437830 0.197992
#>
#>
#> Scale parameter:
#> lower upper
#> Bedon.Bedon 0.87758 1.3019
#> Bedon.LaPlata 0.97158 1.7093
#> LaPlata.LaPlata 5.29540 7.8042
#>
#>
#> Regime 3
#>
#>
#> Autoregressive coefficients:
#> lower upper
#> Bedon:(Intercept) 3.9894134 7.294559
#> LaPlata:(Intercept) 11.1902226 23.020733
#> Bedon:Bedon.lag(1) 0.3082183 0.643302
#> LaPlata:Bedon.lag(1) 0.0618211 1.074497
#> Bedon:LaPlata.lag(1) 0.0093636 0.076327
#> LaPlata:LaPlata.lag(1) 0.2114032 0.464734
#> Bedon:Bedon.lag(2) -0.0640751 0.223012
#> LaPlata:Bedon.lag(2) -1.1002530 -0.019625
#> Bedon:LaPlata.lag(2) -0.0375335 0.031024
#> LaPlata:LaPlata.lag(2) -0.0098455 0.260123
#> Bedon:Bedon.lag(3) -0.2215945 0.040573
#> LaPlata:Bedon.lag(3) -1.0350627 -0.113238
#> Bedon:LaPlata.lag(3) -0.0021404 0.073491
#> LaPlata:LaPlata.lag(3) 0.1369960 0.447739
#> Bedon:Bedon.lag(4) -0.1471890 0.137477
#> LaPlata:Bedon.lag(4) -0.5163643 0.626348
#> Bedon:LaPlata.lag(4) -0.0369771 0.046311
#> LaPlata:LaPlata.lag(4) -0.1695370 0.140870
#> Bedon:Bedon.lag(5) 0.0371031 0.309065
#> LaPlata:Bedon.lag(5) -0.1978915 0.788498
#> Bedon:LaPlata.lag(5) -0.0475675 0.022435
#> LaPlata:LaPlata.lag(5) -0.0688179 0.196537
#>
#>
#> Scale parameter:
#> lower upper
#> Bedon.Bedon 2.2380 3.3653
#> Bedon.LaPlata 5.3841 8.9079
#> LaPlata.LaPlata 33.7646 52.2459
###### 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=1000, n.sim=2000,
n.thin=2, dist="Slash")
coda::HPDinterval(fit3)
#>
#> Probability = 0.95
#>
#> Thresholds:
#> lower upper
#> Threshold.1 1.1008 1.2955
#>
#>
#> Regime 1
#>
#>
#> Autoregressive coefficients:
#> lower upper
#> Jokulsa:(Intercept) 3.01216112 4.44910666
#> Vatnsdalsa:(Intercept) 0.44668018 1.22930933
#> Jokulsa:Jokulsa.lag( 1) 0.77506393 0.91634416
#> Vatnsdalsa:Jokulsa.lag( 1) -0.10235444 -0.02407218
#> Jokulsa:Vatnsdalsa.lag( 1) 0.09998825 0.32709513
#> Vatnsdalsa:Vatnsdalsa.lag( 1) 1.08389466 1.24401168
#> Jokulsa:Jokulsa.lag( 2) -0.09951992 -0.00281991
#> Vatnsdalsa:Jokulsa.lag( 2) 0.01689143 0.08022687
#> Jokulsa:Vatnsdalsa.lag( 2) -0.30479455 -0.04746716
#> Vatnsdalsa:Vatnsdalsa.lag( 2) -0.38602776 -0.21802254
#> Jokulsa:Jokulsa.lag( 3) -0.03149461 0.04139062
#> Vatnsdalsa:Jokulsa.lag( 3) -0.04881667 -0.00140318
#> Jokulsa:Vatnsdalsa.lag( 3) -0.03449313 0.09827377
#> Vatnsdalsa:Vatnsdalsa.lag( 3) -0.01611032 0.08225878
#> Jokulsa:Jokulsa.lag( 4) -0.04499217 0.04141711
#> Vatnsdalsa:Jokulsa.lag( 4) -0.01165285 0.04086384
#> Jokulsa:Vatnsdalsa.lag( 4) -0.06259357 0.08832019
#> Vatnsdalsa:Vatnsdalsa.lag( 4) -0.05280651 0.05646114
#> Jokulsa:Jokulsa.lag( 5) -0.05363857 0.04941220
#> Vatnsdalsa:Jokulsa.lag( 5) -0.02085180 0.03710989
#> Jokulsa:Vatnsdalsa.lag( 5) -0.10744124 0.02592505
#> Vatnsdalsa:Vatnsdalsa.lag( 5) -0.06351926 0.02606703
#> Jokulsa:Jokulsa.lag( 6) -0.02955569 0.07324768
#> Vatnsdalsa:Jokulsa.lag( 6) -0.02457565 0.03424973
#> Jokulsa:Vatnsdalsa.lag( 6) -0.07929339 0.04304337
#> Vatnsdalsa:Vatnsdalsa.lag( 6) -0.03622490 0.04064777
#> Jokulsa:Jokulsa.lag( 7) -0.04782558 0.05946626
#> Vatnsdalsa:Jokulsa.lag( 7) -0.03190665 0.02715151
#> Jokulsa:Vatnsdalsa.lag( 7) -0.04203596 0.07437360
#> Vatnsdalsa:Vatnsdalsa.lag( 7) -0.02836016 0.04507614
#> Jokulsa:Jokulsa.lag( 8) -0.05281753 0.05013904
#> Vatnsdalsa:Jokulsa.lag( 8) -0.03412357 0.01741990
#> Jokulsa:Vatnsdalsa.lag( 8) -0.06173290 0.05484411
#> Vatnsdalsa:Vatnsdalsa.lag( 8) -0.02826708 0.04573722
#> Jokulsa:Jokulsa.lag( 9) -0.05338480 0.03580947
#> Vatnsdalsa:Jokulsa.lag( 9) -0.00761687 0.05052029
#> Jokulsa:Vatnsdalsa.lag( 9) -0.07209249 0.05646874
#> Vatnsdalsa:Vatnsdalsa.lag( 9) -0.04564142 0.03617924
#> Jokulsa:Jokulsa.lag(10) -0.00742436 0.06156446
#> Vatnsdalsa:Jokulsa.lag(10) -0.03706139 0.00776789
#> Jokulsa:Vatnsdalsa.lag(10) -0.03125298 0.09160990
#> Vatnsdalsa:Vatnsdalsa.lag(10) -0.02026314 0.06519512
#> Jokulsa:Jokulsa.lag(11) -0.04241130 0.01764877
#> Vatnsdalsa:Jokulsa.lag(11) -0.01023831 0.02735578
#> Jokulsa:Vatnsdalsa.lag(11) -0.07431870 0.03723784
#> Vatnsdalsa:Vatnsdalsa.lag(11) -0.04663509 0.03196626
#> Jokulsa:Jokulsa.lag(12) -0.01950480 0.03958562
#> Vatnsdalsa:Jokulsa.lag(12) -0.02673506 0.01147259
#> Jokulsa:Vatnsdalsa.lag(12) -0.04277132 0.06048036
#> Vatnsdalsa:Vatnsdalsa.lag(12) -0.03745907 0.03180228
#> Jokulsa:Jokulsa.lag(13) -0.05358290 0.01440206
#> Vatnsdalsa:Jokulsa.lag(13) -0.01621208 0.02280159
#> Jokulsa:Vatnsdalsa.lag(13) -0.06514952 0.03727700
#> Vatnsdalsa:Vatnsdalsa.lag(13) -0.06104591 0.01884027
#> Jokulsa:Jokulsa.lag(14) -0.02202848 0.03331771
#> Vatnsdalsa:Jokulsa.lag(14) -0.02354132 0.01275104
#> Jokulsa:Vatnsdalsa.lag(14) -0.04550223 0.06128400
#> Vatnsdalsa:Vatnsdalsa.lag(14) -0.00351401 0.07006321
#> Jokulsa:Jokulsa.lag(15) -0.00781469 0.04756129
#> Vatnsdalsa:Jokulsa.lag(15) -0.01388953 0.01514017
#> Jokulsa:Vatnsdalsa.lag(15) -0.05336082 0.02749317
#> Vatnsdalsa:Vatnsdalsa.lag(15) -0.02156065 0.03381161
#> Jokulsa:Precipitation.lag(1) -0.01121017 0.02457475
#> Vatnsdalsa:Precipitation.lag(1) -0.00525892 0.01730374
#> Jokulsa:Precipitation.lag(2) -0.00878198 0.01835785
#> Vatnsdalsa:Precipitation.lag(2) -0.00888620 0.00843741
#> Jokulsa:Precipitation.lag(3) -0.02269249 -0.00078006
#> Vatnsdalsa:Precipitation.lag(3) -0.01191608 0.00341215
#> Jokulsa:Precipitation.lag(4) 0.00631820 0.03176934
#> Vatnsdalsa:Precipitation.lag(4) -0.00493681 0.01360282
#> Jokulsa:Temperature.lag(1) 0.00067557 0.04277496
#> Vatnsdalsa:Temperature.lag(1) -0.01051131 0.01609708
#> Jokulsa:Temperature.lag(2) -0.05885582 -0.01694403
#> Vatnsdalsa:Temperature.lag(2) -0.02557699 0.00072988
#>
#>
#> Scale parameter:
#> lower upper
#> Jokulsa.Jokulsa 0.0487295 0.086925
#> Jokulsa.Vatnsdalsa 0.0057223 0.016686
#> Vatnsdalsa.Vatnsdalsa 0.0205724 0.036612
#>
#>
#> Regime 2
#>
#>
#> Autoregressive coefficients:
#> lower upper
#> Jokulsa:(Intercept) -1.5723999 1.17426201
#> Vatnsdalsa:(Intercept) 0.3176978 0.68149696
#> Jokulsa:Jokulsa.lag( 1) 0.9468790 1.08749796
#> Vatnsdalsa:Jokulsa.lag( 1) -0.0096881 0.00479818
#> Jokulsa:Vatnsdalsa.lag( 1) 0.4146019 1.35134422
#> Vatnsdalsa:Vatnsdalsa.lag( 1) 1.1046851 1.24589024
#> Jokulsa:Jokulsa.lag( 2) -0.3015312 -0.05110430
#> Vatnsdalsa:Jokulsa.lag( 2) -0.0023161 0.02199606
#> Jokulsa:Vatnsdalsa.lag( 2) -1.0766025 0.30767417
#> Vatnsdalsa:Vatnsdalsa.lag( 2) -0.4416007 -0.25139746
#> Jokulsa:Jokulsa.lag( 3) -0.0953003 0.12478641
#> Vatnsdalsa:Jokulsa.lag( 3) -0.0241543 0.00054486
#> Jokulsa:Vatnsdalsa.lag( 3) -0.6221723 0.68931177
#> Vatnsdalsa:Vatnsdalsa.lag( 3) 0.1058766 0.27674337
#> Jokulsa:Jokulsa.lag( 4) -0.1603535 0.01032929
#> Vatnsdalsa:Jokulsa.lag( 4) -0.0037103 0.01476837
#> Jokulsa:Vatnsdalsa.lag( 4) -0.6506232 0.29530082
#> Vatnsdalsa:Vatnsdalsa.lag( 4) -0.1592454 -0.00126648
#> Jokulsa:Jokulsa.lag( 5) -0.0385360 0.11645162
#> Vatnsdalsa:Jokulsa.lag( 5) -0.0149819 0.00371398
#> Jokulsa:Vatnsdalsa.lag( 5) -0.6203686 0.62775905
#> Vatnsdalsa:Vatnsdalsa.lag( 5) -0.0857835 0.10574123
#> Jokulsa:Jokulsa.lag( 6) -0.1052252 0.01974520
#> Vatnsdalsa:Jokulsa.lag( 6) -0.0047613 0.01193621
#> Jokulsa:Vatnsdalsa.lag( 6) -0.4951375 0.71809928
#> Vatnsdalsa:Vatnsdalsa.lag( 6) -0.0707261 0.12346937
#> Jokulsa:Jokulsa.lag( 7) -0.0602005 0.05638895
#> Vatnsdalsa:Jokulsa.lag( 7) -0.0140332 0.00274295
#> Jokulsa:Vatnsdalsa.lag( 7) -0.3470259 0.54083718
#> Vatnsdalsa:Vatnsdalsa.lag( 7) -0.1184494 0.00963466
#> Jokulsa:Jokulsa.lag( 8) -0.0407069 0.07351912
#> Vatnsdalsa:Jokulsa.lag( 8) -0.0037274 0.01195866
#> Jokulsa:Vatnsdalsa.lag( 8) -0.6947072 0.21922124
#> Vatnsdalsa:Vatnsdalsa.lag( 8) -0.1042657 0.02067240
#> Jokulsa:Jokulsa.lag( 9) -0.0199993 0.10376144
#> Vatnsdalsa:Jokulsa.lag( 9) -0.0098786 0.00672259
#> Jokulsa:Vatnsdalsa.lag( 9) -0.2992893 0.55651690
#> Vatnsdalsa:Vatnsdalsa.lag( 9) 0.0174975 0.16059199
#> Jokulsa:Jokulsa.lag(10) -0.0984319 0.05595441
#> Vatnsdalsa:Jokulsa.lag(10) -0.0060387 0.01295489
#> Jokulsa:Vatnsdalsa.lag(10) -0.4075627 0.36245157
#> Vatnsdalsa:Vatnsdalsa.lag(10) -0.1261248 -0.01086358
#> Jokulsa:Jokulsa.lag(11) -0.0790049 0.07680963
#> Vatnsdalsa:Jokulsa.lag(11) -0.0158582 0.00231287
#> Jokulsa:Vatnsdalsa.lag(11) -0.5615513 0.45983671
#> Vatnsdalsa:Vatnsdalsa.lag(11) 0.0076393 0.14697657
#> Jokulsa:Jokulsa.lag(12) -0.0788102 0.06739931
#> Vatnsdalsa:Jokulsa.lag(12) 0.0010255 0.01800121
#> Jokulsa:Vatnsdalsa.lag(12) -0.4693305 0.46357053
#> Vatnsdalsa:Vatnsdalsa.lag(12) -0.1430989 -0.02646569
#> Jokulsa:Jokulsa.lag(13) -0.0890793 0.07088979
#> Vatnsdalsa:Jokulsa.lag(13) -0.0143842 0.00160358
#> Jokulsa:Vatnsdalsa.lag(13) -0.0823032 0.90049477
#> Vatnsdalsa:Vatnsdalsa.lag(13) 0.0725195 0.21115161
#> Jokulsa:Jokulsa.lag(14) -0.0856602 0.06853469
#> Vatnsdalsa:Jokulsa.lag(14) -0.0105884 0.00626477
#> Jokulsa:Vatnsdalsa.lag(14) -0.3887712 0.63335348
#> Vatnsdalsa:Vatnsdalsa.lag(14) -0.1308672 0.02422233
#> Jokulsa:Jokulsa.lag(15) 0.0040650 0.09222334
#> Vatnsdalsa:Jokulsa.lag(15) -0.0045390 0.00647517
#> Jokulsa:Vatnsdalsa.lag(15) -0.7344939 -0.11163292
#> Vatnsdalsa:Vatnsdalsa.lag(15) -0.0644018 0.03515953
#> Jokulsa:Precipitation.lag(1) -0.1922928 -0.03924551
#> Vatnsdalsa:Precipitation.lag(1) -0.0128229 0.00751237
#> Jokulsa:Precipitation.lag(2) -0.0992820 0.15022712
#> Vatnsdalsa:Precipitation.lag(2) -0.0157662 0.01159919
#> Jokulsa:Precipitation.lag(3) -0.0162778 0.11219349
#> Vatnsdalsa:Precipitation.lag(3) -0.0034313 0.01637317
#> Jokulsa:Precipitation.lag(4) -0.0394901 0.09785084
#> Vatnsdalsa:Precipitation.lag(4) -0.0059753 0.01150767
#> Jokulsa:Temperature.lag(1) 0.9292779 1.31060648
#> Vatnsdalsa:Temperature.lag(1) -0.0028265 0.04685542
#> Jokulsa:Temperature.lag(2) -0.7683949 -0.35371213
#> Vatnsdalsa:Temperature.lag(2) -0.0536449 -0.00166153
#>
#>
#> Scale parameter:
#> lower upper
#> Jokulsa.Jokulsa 0.959662 1.781023
#> Jokulsa.Vatnsdalsa 0.024084 0.069135
#> Vatnsdalsa.Vatnsdalsa 0.015911 0.030668
#>
#>
#> Extra parameter:
#> lower upper
#> nu 0.72757 0.89162
###### 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=1000,
n.sim=2000, n.thin=2, dist="Student-t")
coda::HPDinterval(fit4)
#>
#> Probability = 0.95
#>
#> Thresholds:
#> lower upper
#> Threshold.1 1.1487 2.2008
#>
#>
#> Regime 1
#>
#>
#> Autoregressive coefficients:
#> lower upper
#> CCR:(Intercept) 0.0677208 0.11502896
#> CCR:CCR.lag(1) -0.0780972 -0.02240717
#> CCR:CCR.lag(2) -0.0826999 0.00020929
#> CCR:CCR.lag(3) -0.0664100 0.01755325
#> CCR:dVIX.lag(1) -0.0627957 -0.00958405
#> CCR:dVIX.lag(2) -0.0486701 0.00520388
#> CCR:dVIX.lag(3) -0.0011461 0.03140187
#>
#>
#> Scale parameter:
#> lower upper
#> CCR.CCR 0.33502 0.40059
#>
#>
#> Regime 2
#>
#>
#> Autoregressive coefficients:
#> lower upper
#> CCR:(Intercept) -0.4090450 0.2460139
#> CCR:CCR.lag(1) -0.3216915 0.0067886
#> CCR:CCR.lag(2) -0.2320086 0.1935572
#> CCR:CCR.lag(3) -0.0481479 0.3456160
#> CCR:dVIX.lag(1) -0.1451481 0.1306261
#> CCR:dVIX.lag(2) -0.0804351 0.1579721
#> CCR:dVIX.lag(3) -0.0019516 0.1317174
#>
#>
#> Scale parameter:
#> lower upper
#> CCR.CCR 0.73511 1.2924
#>
#>
#> Extra parameter:
#> lower upper
#> nu 2.2401 2.6517
# }