hit.ratio: Computes Hit Ratio (HR) for Forecast.

In fDMA: Dynamic Model Averaging and Dynamic Model Selection for Continuous Outcomes

Computes Hit Ratio (HR) for Forecast.

Description

Sometimes it is interesting to analyze just whether the forecast can predict the direction of a change in a modelled time-series. This function computes the proportion of correctly predicted signs (i.e., in which cases the direction of a change given by forecast agrees with the change in real data).

Usage

hit.ratio(y,y.hat,d=NULL)

Arguments

y	numeric, vector, or one row or one column matrix or xts object, representing a forecasted time-series
y.hat	numeric, vector, or one row or one column matrix or xts object, representing forecast predictions
d	optional, logical, d=FALSE for level time-series, d=TRUE if time-series already represent changes, by default d=FALSE

Value

numeric

References

Baur, D. G., Beckmann, J., Czudaj, R., 2016. A melting pot – Gold price forecasts under model and parameter uncertainty. International Review of Financial Analysis 48, 282–291.

Examples

wti <- crudeoil[-1,1]
drivers <- (lag(crudeoil[,-1],k=1))[-1,]
ld.wti <- (diff(log(wti)))[-1,]
ld.drivers <- drivers[-1,]
ld.drivers[,c(4,6)] <- (diff(drivers[,c(4,6)]))[-1,]
ld.drivers[,c(1:2,5,7)] <- (diff(log(drivers[,c(1:2,5,7)])))[-1,]
ld.drivers[,c(3,6)] <- ld.drivers[,c(3,6)]/100

m1 <- fDMA(y=wti,x=drivers[,5:7],alpha=0.99,lambda=0.99,initvar=10)
hit.ratio(y=wti,y.hat=m1$y.hat)

m2 <- fDMA(y=ld.wti,x=ld.drivers,alpha=0.99,lambda=0.99,initvar=10)
hit.ratio(y=ld.wti,y.hat=m2$y.hat,d=TRUE)

fDMA documentation built on April 4, 2025, 5:16 a.m.