rmafe {bvhar}

Evaluate the Model Based on RMAFE

Description

This function computes RMAFE (Mean Absolute Forecast Error Relative to the Benchmark)

Usage

rmafe(x, pred_bench, y, ...)

## S3 method for class 'predbvhar'
rmafe(x, pred_bench, y, ...)

## S3 method for class 'bvharcv'
rmafe(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. RMAFE is the ratio of L1 norm of e_t from forecasting object and from benchmark model.

RMAFE = \frac{sum(\lVert e_t \rVert)}{sum(\lVert e_t^{(b)} \rVert)}

where e_t^{(b)} is the error from the benchmark model.

Value

RMAFE 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).