tc.test: Computes Test for Time Clusters.

In pEPA: Tests of Equal Predictive Accuracy for Panels of Forecasts

Computes Test for Time Clusters.

Description

This function computes test of the equal predictive accuracy for time clusters. The null hypothesis of this test is that the equal predictive accuracy for the two methods holds within each of the time clusters. The test is suitable if either: K \ge 2 and significance level \le 0.08326, or 2 \le K \leq 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

tc.test(evaluated1,evaluated2,realized,loss.type="SE",cl)

Arguments

evaluated1	same as in pool_av.test
evaluated2	same as in pool_av.test
realized	same as in pool_av.test
loss.type	same as in pool_av.test
cl	vector of the beginning indices of each pre-defined blocks of time – as a result always cl[1]=1

Value

class htest object, list of

statistic	test statistic
parameter	K, number of time 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]
  }
# 3 time clusters: Jun 1996 -- Nov 2007, Dec 2007 -- Jun 2009, Jul 2009 - Aug 2021
# rownames(observed)[1] 
# rownames(observed)[139] 
# rownames(observed)[158] 
t.cl <- c(1,139,158)
t <- tc.test(evaluated1=f.bsr,evaluated2=f.dma,realized=y,loss.type="SE",cl=t.cl)

pEPA documentation built on April 3, 2025, 7:08 p.m.