Error when using msmFit in R - r

Question

I'm trying to simulate this paper (Point Forecast Markov Switching Model for U.S. Dollar/ Euro Exchange Rate, by Hamidreza Mostafei) in R. The table that I'm trying to get is on page 483. Here is a link to a pdf.
I wrote the following codes and then got an error at the last line:
mydata <- read.csv("C:\\Users\\User\\Downloads\\EURUSD_2.csv", header=T)
mod <- lm(EURUSD~EURUSD.1, mydata)
mod.mswm = msmFit(mod, k=2, p=1, sw=c(T,T,T,T), control=list(parallel=F))
Error in if ((max(abs(object["Fit"]["logLikel"] - oldll))/(0.1 + max(abs(object["Fit"]["logLikel"]))) < :
missing value where TRUE/FALSE needed
Basically the data that's being used is EURUSD, which is the level change in monthly frequency. EURUSD.1 is the one lag variable. Both EURUSD and EURUSD.1 are in my csv file. (I'm not sure how to attach the csv file here. If someone could point that out that would be great).
I changed the EURUSD.1 values to something random and msmFit function seemed to work. But whenever I tried using the original value, i.e. the lag value, the error came out.

Answer 1

Something degenerate is happening when one variable is simply lagged from the other. Consider:
Sample data frame where Y is lagged X:
> d = data.frame(X=runif(100))
> d$Y=c(.5, d$X[-100])
> mod <- lm(X~Y,d)
> mod.mswm = msmFit(mod, k=2, p=1, sw=c(T,T,T,T), control=list(parallel=F))
Error in if ((max(abs(object["Fit"]["logLikel"] - oldll))/(0.1 + max(abs(object["Fit"]["logLikel"]))) < :
missing value where TRUE/FALSE needed
that gives your error. Let's add a tiny tiny bit of noise to Y and see what happens:
> d$Y=d$Y+rnorm(100,0,.000001)
> mod <- lm(X~Y,d)
> mod.mswm = msmFit(mod, k=2, p=1, sw=c(T,T,T,T), control=list(parallel=F))
> mod.mswm
Markov Switching Model
Call: msmFit(object = mod, k = 2, sw = c(T, T, T, T), p = 1, control = list(parallel = F))
AIC BIC logLik
4.3109 47.45234 3.84455
Coefficients:
(Intercept)(S) Y(S) X_1(S) Std(S)
Model 1 0.8739622 -22948.89 22948.83 0.08194545
Model 2 0.4220748 77625.21 -77625.17 0.21780764
Transition probabilities:
Regime 1 Regime 2
Regime 1 0.3707261 0.3886715
Regime 2 0.6292739 0.6113285
It works! Now either:
Having perfectly lagged variables causes some "divide by zero" error because its a purely degenerate case (like having perfectly co-linear variables in a linear model). A little experimenting shows that in this case the resulting output is very sensitive to how much noise you add, so I'm thinking its on a knife-edge here. I suspect having perfectly lagged variables here leads to some singularity or degeneracy.
or
There's some bug in the function.
I have no idea what msmFit does, so that's for you to sort out.