csc.test {pEPA}

Computes Test for Cross-Sectional Clusters.

Description

This function computes test of the equal predictive accuracy for cross-sectional clusters. The null hypothesis of this test is that a pair of forecasts have the same expected accuracy among cross-sectional clusters. However, their predictive accuracy can be different across the clusters, but the same among each cluster. The test is suitable if either: K \ge 2 and significance level \le 0.08326, or 2 \le K \le 14 and significance level \le 0.1, or K = \{ 2,3 \} and significance level \le 0.2, where K denotes the number of time clusters.

Usage

csc.test(evaluated1,evaluated2,realized,loss.type="SE",cl,dc=FALSE)

Arguments

evaluated1

same as in pool_av.test, but cross-sections are ordered rowwise

evaluated2

same as in pool_av.test, but cross-sections are ordered rowwise

realized

same as in pool_av.test, but cross-sections are ordered rowwise

loss.type

same as in pool_av.test

cl

vector of the beginning indices of rows for each pre-defined clusters – as a result always cl[1]=1

dc

logical indicating if apply decorrelating clusters, if not specified dc=FALSE is used

Value

class htest object, list of

statistic

test statistic

parameter

K, number of cross-sectional clusters

alternative

alternative hypothesis of the test

p.value

p-value

method

name of the test

data.name

names of the tested data

References

Qu, R., Timmermann, A., Zhu, Y. 2024. Comparing forecasting performance with panel data. International Journal of Forecasting 40, 918–941.

See Also

pool_av.test

Examples

data(forecasts)
y <- t(observed)
f.bsr <- matrix(NA,ncol=ncol(y),nrow=56)
f.dma <- f.bsr
# extract prices predicted by BSR rec and DMA methods
for (i in 1:56)
  {
    f.bsr[i,] <- predicted[[i]][,1]
    f.dma[i,] <- predicted[[i]][,9]
  }
# 2 cross-sectional clusters: energy commodities and non-energy commodities
cs.cl <- c(1,9)
t <- csc.test(evaluated1=f.bsr,evaluated2=f.dma,realized=y,loss.type="SE",cl=cs.cl)