fDMA (version 2.2.7)

hmdmtest: Computes Diebold-Mariano Test when Presence of ARCH Effects is Suspected.

Description

This is a wrapper for dm.test from forecast package. This function computes the modified Diebold-Mariano test. The modification is useful if the presence of ARCH effects is suspected in forecast errors. It is also useful for small samples. This is a modification of mdmtest for the presence of ARCH effects in forecast errors.

Usage

hmdmtest(y,f)

Value

matrix, first column contains tests statistics, next p-values are given for the alternative hypothesis that alternative forecasts have different accuracy than the compared forecast, alternative forecasts are less accurate and alternative forecasts have greater accuracy, tests outcomes for different forecasts are ordered by rows

Arguments

y

vector of the forecasted time-series

f

matrix of the predicted values from various methods, forecasts are ordered in rows, the first row should correspond to the method that is compared with alternative ones (corresponding to subsequent rows)

Details

The null hypothesis is that the two methods have the same forecast accuracy. This function assumes that one-step ahead forecasts are compared and the second power is used in the loss function (see dm.test).

References

Newbold, P., Harvey, D. J., 2002. Forecast combinations. In: Clements, M. P., Hendry, D. F. (eds.), A Companion to Economic Forecasting, Blackwell Publishing Ltd.

See Also

archtest, dmtest, mdmtest.

Examples

# \donttest{
wti <- crudeoil[-1,1]
drivers <- (lag(crudeoil[,-1],k=1))[-1,]
ld.wti <- (diff(log(wti)))[-1,]
ld.drivers <- (diff(log(drivers)))[-1,]
m <- fDMA(y=ld.wti,x=ld.drivers,alpha=0.99,lambda=0.90,initvar=10)
m <- m$y.hat
a <- altf2(y=ld.wti,x=ld.drivers,d=TRUE)
a <- a$y.hat
a <- matrix(unlist(a),nrow=length(a),byrow=TRUE)
fc <- rbind(m,a)
hmdm <- hmdmtest(y=as.vector(ld.wti),f=fc)
# }