regimes.plot.MSAR: Plot MSAR time series with regimes

In NHMSAR: Non-Homogeneous Markov Switching Autoregressive Models

Description

Plot MSAR time series with regimes materialized by gray boxes.

Usage

regimes.plot.MSAR(res, data, ex = 1, col.l = "red", nc = 1, 
ylim = NULL, xlab = "time", ylab = "series", d = NULL, dt = 1, lwd = 1, cex = 1)

Arguments

res	list obtained from fit.MSAR fonction as result of MSAR fitting
data	data to plot
ex	number of sample
nc	component number (useful for multivariate time series)
col.l	color of time series (default is red)
ylim	range for the plotted 'y' values, defaulting to the range of the finite values of 'y'
xlab	a title for the x axis
ylab	a title for the y axis
d	dimension to be plot (for multivariate cases). Default is 1.
dt	time step (default=1)
lwd	width of the line
cex	symbols/text size

Value

Returns a plot and the regimes time series.

Author(s)

Valerie Monbet, [e-mail]

Examples

data(lynx)
T = length(lynx)
data = array(log(lynx),c(T,1,1))
theta.init = init.theta.MSAR(data,M=2,order=2,label="HH")
mod.lynx = fit.MSAR(data,theta.init)
regimes.plot.MSAR(mod.lynx,data,ylab="Captures number")

theta.init = init.theta.MSAR(data,M=2,order=2,label="NH",nh.transitions="logistic")
attributes(theta.init)
theta.init$A0 = mod.lynx$theta$A0
theta.init$A = mod.lynx$theta$A
theta.init$sigma = mod.lynx$theta$sigma
theta.init$prior = mod.lynx$theta$prior
theta.init$transmat = mod.lynx$theta$transmat
theta.init$par.trans = matrix(c(1,-1,-.2,.2),2,2)
Y = array(data[2:T,,],c(T-1,1,1))
Z = array(data[2:T,,],c(T-1,1,1))
mod.lynx = fit.MSAR(Y,theta.init,verbose=TRUE,MaxIter=20,covar.trans=Z)
regimes.plot.MSAR(mod.lynx,data,ylab="Captures number")

## Not run
# Fit Homogeneous MS-AR models - multivariate time series
#data(PibDetteDemoc)
#T = length(unique(PibDetteDemoc$year))-1
#N.samples = length(unique(PibDetteDemoc$country))
#PIB = matrix(PibDetteDemoc$PIB,N.samples,T+1)
#Dette = matrix(PibDetteDemoc$Dette,N.samples,T+1)
#Democratie = matrix(PibDetteDemoc$Democratie,N.samples,T+1)

#d = 2
#Y = array(0,c(T,N.samples,2))
#for (k in 1:N.samples) {
#   Y[,k,1] = diff(log(PIB[k,]))
#   Y[,k,2] = diff(log(Dette[k,]))
#}
#Democ = Democratie[,2:(T+1)] 
#theta.hh.1 = init.theta.MSAR(Y,M=4,order=1,label="HH")
#res.hh = fit.MSAR(Y,theta.hh.1,verbose=TRUE,MaxIter=200)
#par(mfrow=c(2,1))
#regimes.plot.MSAR(res.hh,Y,ex=30,ylab="GDP")
#regimes.plot.MSAR(res.hh,Y,ex=30,nc=2,ylab="Debt")

NHMSAR documentation built on Feb. 9, 2022, 9:06 a.m.