rmsfe {bvhar} | R Documentation |
Evaluate the Model Based on RMSFE
Description
This function computes RMSFE (Mean Squared Forecast Error Relative to the Benchmark)
Usage
rmsfe(x, pred_bench, y, ...)
## S3 method for class 'predbvhar'
rmsfe(x, pred_bench, y, ...)
## S3 method for class 'bvharcv'
rmsfe(x, pred_bench, y, ...)
Arguments
x |
Forecasting object to use |
pred_bench |
The same forecasting object from benchmark model |
y |
Test data to be compared. should be the same format with the train data. |
... |
not used |
Details
Let .
RMSFE is the ratio of L2 norm of
from forecasting object and from benchmark model.
where is the error from the benchmark model.
Value
RMSFE vector corresponding to each variable.
References
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688.
Bańbura, M., Giannone, D., & Reichlin, L. (2010). Large Bayesian vector auto regressions. Journal of Applied Econometrics, 25(1).
Ghosh, S., Khare, K., & Michailidis, G. (2018). High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models. Journal of the American Statistical Association, 114(526).