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 e_t = y_t - \hat{y}_t
.
RMSFE is the ratio of L2 norm of e_t
from forecasting object and from benchmark model.
RMSFE = \frac{sum(\lVert e_t \rVert)}{sum(\lVert e_t^{(b)} \rVert)}
where e_t^{(b)}
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).