pool_av.S1.test: Computes Test for Overall Equal Predictive Ability.

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

Computes Test for Overall Equal Predictive Ability.

Description

This function computes test of the equal predictive accuracy for the pooled average. It corresponds to S^{(1)}_{nT} statistic in the referenced paper by Akgun et al. (2024). The null hypothesis of this test is that the pooled average loss is equal in expectation for a pair of forecasts from both considered methods. The alternative one is that the differences do not average out across the cross-sectional and time-series dimensions. The test is suitable for situations with cross-sectional independence.

Usage

pool_av.S1.test(evaluated1,evaluated2,realized,loss.type="SE")

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

Value

class htest object, list of

statistic	test statistic
alternative	alternative hypothesis of the test
p.value	p-value
method	name of the test
data.name	names of the tested data

References

Akgun, O., Pirotte, A., Urga, G., Yang, Z. 2024. Equal predictive ability tests based on panel data with applications to OECD and IMF forecasts. International Journal of Forecasting 40, 202–228.

See Also

pool_av.test, pool_av.S3.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]
  }
t <- pool_av.S1.test(evaluated1=f.bsr,evaluated2=f.dma,realized=y,loss.type="SE")

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