pool_av.test {pEPA} | R Documentation |
This function computes test of the equal predictive accuracy for the pooled average. 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 hypothesis can be formulated as the differences do not average out across the cross-sectional and time-series dimensions.
pool_av.test(evaluated1,evaluated2,realized,loss.type="SE",J=NULL)
evaluated1 |
|
evaluated2 |
|
realized |
|
loss.type |
a method to compute the loss function, |
J |
|
class htest
object, list
of
statistic |
test statistic |
parameter |
|
alternative |
alternative hypothesis of the test |
p.value |
p-value |
method |
name of the test |
data.name |
names of the tested data |
Hyndman, R.J., Koehler, A.B. 2006. Another look at measures of forecast accuracy. International Journal of Forecasting 22, 679–688.
Qu, R., Timmermann, A., Zhu, Y. 2024. Comparing forecasting performance with panel data. International Journal of Forecasting 40, 918–941.
Taylor, S. J., 2005. Asset Price Dynamics, Volatility, and Prediction, Princeton University Press.
Triacca, U., 2024. Comparing Predictive Accuracy of Two Forecasts, https://www.lem.sssup.it/phd/documents/Lesson19.pdf.
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.test(evaluated1=f.bsr,evaluated2=f.dma,realized=y,loss.type="SE")