Evaluate the Model Based on RMSFE

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

Value

RMSFE vector corresponding to each variable.

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.

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).