MDM.selection function

Selects Models with Outstanding Predictive Ability basing on Multivariate Diebold-Mariano Test.

Selects Models with Outstanding Predictive Ability basing on Multivariate Diebold-Mariano Test.

This function selects models with outstanding predictive ability basing on multivariate Diebold-Mariano test MDM.test.

MDM.selection(realized,evaluated,q,alpha,statistic="Sc",loss.type="SE")

Arguments

  • realized: vector of the real values of the modelled time-series
  • evaluated: matrix of the forecasts, columns correspond to time index, rows correspond to different models
  • q: numeric indicating a lag length beyond which we are willing to assume that the autocorrelation of loss differentials is essentially zero
  • alpha: numeric indicating a significance level for multivariate Diebold-Mariano tests
  • statistic: statistic="S" for the basic version of the test, and statistic="Sc" for the finite-sample correction, if not specified statistic="Sc" is used
  • loss.type: method to compute the loss function, loss.type="SE" will use squared errors, loss.type="AE" will use absolute errors, loss.type="SPE" will use squred proportional error (useful if errors are heteroskedastic), loss.type="ASE" will use absolute scaled error, if loss.type will be specified as some numeric, then the function of type exp(loss.type*errors)-1-loss.type*errors will be used (useful when it is more costly to underpredict realized than to overpredict), if not specified loss.type="SE" is used

Returns

class MDM object, list of - outcomes: matrix with mean losses for the selected models, statistics corresponding to losses differentials and ranking of these statistics

  • p.value: numeric of p-value from the procedure, i.e., p-value of multivariate Diebold-Mariano test from the last step

  • alpha: alpha, i.e., the chosen significance level

  • eliminated: numeric indicating the number of eliminated models

Examples

data(MDMforecasts)
ts <- MDMforecasts$ts
forecasts <- MDMforecasts$forecasts
MDM.selection(realized=ts,evaluated=forecasts,q=10,alpha=0.1,statistic="S",loss.type="AE")

References

Mariano R.S., Preve, D., 2012. Statistical tests for multiple forecast comparison. Journal of Econometrics 169 , 123--130.

multDM packageRead PDF manual